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Predicting Customer Satisfaction based on Neutrosophic sets: Applied on mobile food ordering application

Neutrosophic Sets and systems 2025年 80卷 214-228页

作者： Elsanabary, Walaa Radwan, Nouran M. Computer and Information Systems Dept. Sadat Academy for Management Sciences Egypt

Customers’ satisfaction prediction is a vital process for all business organizations to draw new customers and maintain existing customers. One of the most efficient methods to predict customers' satisfaction is classifying customers' feedback. In the real world, customer’s feedback is ambiguous, confusing and inconsistent. This option can be stated in neutrosophic logic as indeterminacy membership, associated with truth and falsity membership. In this study, a classification model based on neutrosophic sets to handle the inconsistency of customer responses is presented. Also, significant factors that impact customers' satisfaction are defined. In order to show the procedures and the application of the proposed method, a case study to determine the customers' satisfaction while using food order application (Talabat) in Egypt is presented. A comparison between the classical classification models and the proposed model based on neutrosophic sets is presented. The experimental results indicate that the proposed classifying model achieved accuracy results around 95.36% to 99.95%, higher than the classical one that achieve around 90.3% to 93.99%. Next, a sensitivity analysis is performed for reliability validation as to determine the most factors that affect customers’ satisfaction. © 2025, University of New Mexico. All rights reserved.

关键词： Sales

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computer modeling of the Distribution of Pollutants in the Bed of the Volga River and the Akhtuba River 5

Computer Modeling of the Distribution of Pollutants in the B...

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5th International Conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Baskakov, Eugenii Savin, Egor Kornaukhova, Mariya Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

The process of describing the distribution of impurities is described using two examples: phenomena in the bed of the Volga River in the northern section of the Volga-Akhtuba floodplain without a source under the influence of velocity fields and the flow of impurities from two sources. computer simulation is implemented on the basis of software developed using supercomputer technologies written using the CUDA software and hardware architecture. A mathematical model is proposed that describes the process of transfer of impurities in the aquatic environment. The process of developing and testing a software package is presented. The functionality of the created program allows you to predict the speed and range of propagation of various impurities in a continuous medium with a visual representation. The software package consists of two modules: calculation and visualization. The calculation module is implemented by transferring the computational load to the video card. The graphics module is implemented in Python. The results of numerical modeling of the distribution of pollutants in the Volga River bed are presented. The obtained result shows agreement with the real situation and the results of other researches. © 2023 IEEE.

关键词： Rivers

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Highlighting of Hydrographic Objects on Satellite Images for the Development of High-quality Digital Relief Models of Floodplain Areas as a Basis for modeling the Hydrological Regime 5

Highlighting of Hydrographic Objects on Satellite Images for...

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5th International Conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Tertychnaya, Anna Khoperskov, Alexander Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

Mathematical modeling of surface water dynamics allows us to make forecasts of the hydrological regime of a territory for a wide variety of hydrological, environmental, and geophysical applications. The such simulations quality is largely determined by the relevance of the topographic data used to construct digital terrain models. The important task is the identifying of the boundaries of numerous reservoirs in a floodplain area. The distinctive feature such area is the complex structure of small channels, vegetation, and wetlands. We develop the method for detecting coastlines using multi-channel satellite data which includes two infrared bands (4 and 5 bands), such as Landsat ETM+. Software was tested whith the Northern part of the Volga-Akhtuba Floodplain (Russia) and one of the region in the Amazon basin. It differs significantly in their natural characteristics. During the software testing we determined the conditions that provides accurate detection of borders of water bodies. © 2023 IEEE.

关键词： Reservoirs (water)

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Problem of Improving the Environmental and Socio-Economic System of the Volga-Akhtuba Floodplain: Position Examination of Large Dam in the Volga River Based on Computational Fluid Dynamics 5

Problem of Improving the Environmental and Socio-Economic Sy...

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5th International Conference on Control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Klikunova, Anna Yu. Agafonnikova, Ekaterina O. Khoperskov, Alexander V. Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350309126

The system of hydroelectric power stations on the Volga River, built in the 20th century, completely changed the hydrological regime of the Volga River. The current over-regulation of the water flow is the cause of the accelerating degradation of the Volga-Akhtuba floodplain. Modern analysis of water availability indicates a critical point associated with the spring filling of the Akhtuba binnacle. Various projects suggest a solution to this problem. This study is aims at identifying the most optimal location of a large dam below the Volzhsky hydroelectric power stations. We use a new model of hydraulic resistance based on the combined effect of both the friction force in the Manning model and the turbulent viscosity. Our study involves varying of the dam's location in a numerical shallow water model. We calculate the hydrological relationship between the release of water through the Volzhsky dam and the source of the Akhtuba, as a target function for analyzing the effectiveness of the project to construction such a dam. © 2023 IEEE.

关键词： Dams

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modeling of Extended Flooding of the Volga-Akhtuba Floodplain Under Various Conditions

Modeling of Extended Flooding of the Volga-Akhtuba Floodplai...

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2023 Applied Mathematics, Computational Science and Mechanics: Current Problems, AMCSM 2023

作者： Klikunova, Anna Yu. Dyakonova, Tatyana A. Agafonnikova, Ekaterina O. Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (纸本)9798350348576

A computer model of a extended flood due to an accident at a hydraulic structure is considered. The model is based on the two-dimensional Saint-Venant equations, taking into account the friction and the structure of the terrain on the right side. The integration of the equations is carried out using the Lagrangian-Eulerian CSPH-TVD method (Combined Smooth Particle Hydrodynamics - Total Variation Diminishing). modeling of various scenarios of the destruction of the Volga hydroelectric dam on the dry bottom during the fall and the wet bottom during the seasonal flood was carried out. For calculations, a regular digital terrain model is used, taking into account the channels of the Volga, Akhtuba and the main floodplain peaks. The main parameters of the breakout wave are calculated. Integral characteristics of flooding for the northern part of the Volga-Akhtuba floodplain are obtained. It is shown that hydraulic friction influences the propagation velocity of the breakthrough wave. The simulation results for a given digital model confirm the hypothesis that Volgograd is located in a zone that is not subject to flooding. © 2023 IEEE.

关键词： Hydroelectric power plants

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A Sequential Pattern Mining Approach for Situation-Aware Human Activity Projection

A Sequential Pattern Mining Approach for Situation-Aware Hum...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： D'Aniello, Giuseppe Falcone, Roberto Gaeta, Matteo Rehman, Zia Ur Fortino, Giancarlo University of Salerno Dept. Information and Electrical Engineering and Applied Mathematics SA Fisciano Italy University of Calabria Dept. Computer Engineering Modeling Electronics and Systems Cs Rende Italy

ISBN: (纸本)9781665410205

Human activity prediction has become increasingly prevalent in a plethora of time-critical applications. To realize accurate identification and prediction of human behaviour, we propose a situation-aware wearable computing system. A wearable computing system has the capability to perceive, comprehend and project situations by analyzing the human behavioral patterns in different environments. In particular, this work proposes a situation-aware human activity prediction (SA-HAP) approach based on sequential pattern mining that aims to anticipate future activities and tailor its responses according to situations by analyzing frequent sequential patterns and their correlations to understand how these situations are interrelated. The approach not only improves prediction accuracy but also provide the foundation for a more informed decision-making process, as the projected situations can be explained using the identified behavioral patterns. The approach is compared with other traditional techniques for activity prediction (LSTM and HMM), achieving better performance on the Extrasensory dataset. © 2024 IEEE.

关键词： Prediction models

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Exploring The Use of Advanced Machine Learning to Detect Fraudulent Crypto Platforms 9

Exploring The Use of Advanced Machine Learning to Detect Fra...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Manful, John Terence Hasford, Adelle Yeluripati, Govindha Ramaiah Ashesi University Dept. of Computer Science & Information Systems Berekuso Ghana

ISBN: (纸本)9798350385403

The decentralized nature, self-regulation, and rapid transaction speeds of the cryptocurrency landscape foster both innovation and fraud. As crypto gains attention, fraudulent exchanges posing as legitimate platforms present significant financial risks. Traditional reporting methods, like those of CryptoScamDB, are becoming less effective. This study addresses the need for automated techniques to identify and classify fraudulent exchanges. We found that features related to domain information, website security, and user reviews are crucial for detection. A comparative experiment using machine learning models - random forest, support vector machine, k-nearest neighbour, light gradient boosting machine, and multi-layer perceptron - showed that the light gradient boosting machine (95.9%) and random forest (95.5%) had the highest accuracy. An ensemble model using soft voting achieved a higher accuracy of 96.2%, outperforming its base models. These results demonstrate that machine learning is a viable approach for automating the classification of fraudulent exchanges. © 2024 IEEE.

关键词： Adversarial machine learning

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On Methods for modeling Controlled Dynamics in Stabilization Problems

On Methods for Modeling Controlled Dynamics in Stabilization...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Krasinskiy, Aleksandr Ya. Aitov, Vasily Gr. Dept. of Computer Modeling and Probability Theory Moscow Russia Information and Computing Center Rosbiotech Moscow Russia

Automatic devices make up the vast maj ority of technological equipment in modern production. To obtain an output product of appropriate quality, the most complex program control algorithms can be implemented in actuators, including those found by mathematical control theory methods. Stable given operating mode implementation for controlled systems is ensured by its stabilization. In mathematical control theory, rigorous methods have been developed for solving stabilization problems;the effectiveness of their practical application is determined by the adequacy of the mathematical model of the control object used. The article outlines the main approaches to building models with a discussion of their required accuracy. Specific examples of solving stabilization problems for certain technical systems classes with different orders models of accuracy are given. © 2024 IEEE.

关键词： Stabilization

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Multi-Class Threat Detection Using Neural Network and Machine Learning Approaches in Kubernetes Environments 6

Multi-Class Threat Detection Using Neural Network and Machin...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Aly, Abdelrahman Fayez, Mahmoud Al-Qutt, Mirvat Hamad, Ahmed M. Dept. Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Kubernetes, an open-source platform for automating deployment, scaling, and management of containerized applications, has become a cornerstone in modern IT infrastructure. Alongside its widespread adoption, Kubernetes faces a series of sophisticated security challenges, especially in managing numerous containers. This research uniquely focuses on multi-class threat detection, classifying various types of security threats within Kubernetes environments. Machine learning is emerging as a powerful tool in cybersecurity, offering new ways to detect and mitigate threats. However, there is a shortage of comprehensive research on its application within Kubernetes, especially for detecting multiple types of security threats. This research aims to bridge this gap by introducing a machine learning-based technique for improved threat detection in Kubernetes environments. We propose an advanced detection method using the Naive Bayes algorithm, complemented by comprehensive feature engineering and dimensionality reduction using neural networks. The most effective model, which combines Principal Component Analysis (PCA) and Autoencoder with the Naive Bayes classifier, achieved an Fl Score of 0.95 and an accuracy of 91%. © 2024 IEEE.

关键词： Principal component analysis

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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