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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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Customs Fraud Detection Method Based on Artificial Intelligence and Data Analytics 4

Customs Fraud Detection Method Based on Artificial Intellige...

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4th International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2024

作者： Gancheva, Veska Popov, George Nakov, Ognyan Raynova, Kamelia Popova, Antoaneta Technical University of Sofia Dept. of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Dept. of Cybersecurity Sofia Bulgaria University of Sofia Dept. of Computer Systems Technical Sofia Bulgaria Technical University of Sofia Dept. of Information Technologies in Industry Sofia Bulgaria

ISBN: (纸本)9798350391183

Because of the increased market opening and massive data collection brought about by globalization, the need of maintaining control over customs procedures has increased. However, the integration and processing of customs data are complicated by its extreme imbalance. Finding autonomous, computationally sophisticated solutions for customs handling is therefore crucial. The study described in this paper aims to propose an artificial intelligence-based method for customs fraud detection through data analytics. A machine learning-based approach for detecting customs fraud is proposed. The research methodology includes data preprocessing, data analytics, combining results, and deriving precise solutions from results obtained. The aim is to extract meaningful information from a dataset. For the purpose of classifying customs fraud, seven algorithms for machine-learning-kNN, SGD, Constant, Logistic Regression, Random Forest, Neural Network, and Naive Bayes-Are chosen and empirically assessed. © 2024 IEEE.

关键词： Adversarial machine learning

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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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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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Enhancing LoRaWAN Security: Protection Against Bit Flipping Attacks in IoT Networks 17

Enhancing LoRaWAN Security: Protection Against Bit Flipping ...

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17th International Conference on Security of information and Networks, SIN 2024

作者： Dave, Jay Choudhury, Nikumani Havishteja, Dantu Revanth, Katuri Bits Pilani Dept. of Computer Science & Information Systems Hyderabad Campus India

ISBN: (纸本)9798331509736

The prominence of the Internet of Things (IoT) has surged in recent years due to tech-nological advancements, increased connectivity, and the widespread adoption of smart devices. Currently, IoT solutions are pervasive across various sectors, including smart homes, wearable devices, health-care, transportation, and industrial automation. The Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol designed to provide reliable connection over long distances, spanning many kilometers, using low-power and wide-area networks (LPWANs) often used in IoT applications. However, LoRaWAN is vulnerable to bit-flipping attacks, where transmitted data is intercepted and maliciously altered by flipping specific bits in the message payload. Such attacks have the potential to undermine the trustworthiness and dependability of the system. In this paper, we propose a novel security enhancement for LoRaWAN. Our scheme incorporates a hash digest along with the end device's payload, enabling the application server to detect any malicious alterations in the received content using the hash value. We analyze the security of the proposed scheme against bit-flipping attacks and measure its performance through experiments in a real testbed. Our observations indicate that the proposed mecha-nism not only secures communication against these attacks but also incurs minimal overhead in terms of power consumption, transmission overhead, and latency. © 2024 IEEE.

关键词： Network security

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SonicGlass: An Obstacle Detection and Navigation System Using Smartglass-Based Ultrasonic Sensors 16

SonicGlass: An Obstacle Detection and Navigation System Usin...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Kusneniwar, Hrishikesh Govindrao Ghosh, Surjya Sen, Sougata Bits Pilani Dept. of Computer Science and Information Systems Goa campus India

ISBN: (纸本)9798350383119

Enabling continuous obstacle detection and providing real-time navigational assistance for people with visual impairment allows them to navigate in an indoor space without dependence on others. In this paper, we present the design, development, and evaluation of SonicGlass, a wearable obstacle detection and indoor navigation system. SonicGlass consists of a custom 3D printed wearable smartglass that can replace the individual's existing smartglasses. The smartglass is embedded with multiple sensors and microcontrollers. We performed a user study with 10 participants to determine the performance of SonicGlass and observed that SonicGlass could easily guide individuals in an indoor space while detecting obstacles with an F1-score of 84.7% (at 100% precision), and yielded a localization error of 1.35 meters when using a very simplistic regression model. Overall, SonicGlass can be a helpful tool for people with visual impairment to navigate in indoor spaces without assistance. © 2024 IEEE.

关键词： Accessibility Indoor Navigation Obstacle Detection smartglass Wearable Sensing

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Intelligent Anomaly Detection System Based on Ensemble and Deep Learning 26

Intelligent Anomaly Detection System Based on Ensemble and D...

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Baniya, Babu Kaji Rush, Thomas Bradley University Dept. of Computer Science and Information Systems PeoriaIL United States

ISBN: (纸本)9791188428120

The ubiquity of the Internet plays a pivotal role in connecting individuals and facilitating easy access to various essential services. As of 2022, the International Telecommunication Union (ITU) reports that approximately 5.3 billion people are connected to the internet, underscoring its widespread coverage and indispensability in our daily lives. This expansive coverage enables a myriad of services, including communication, e-banking, e-commerce, online social security access, medical reporting, education, entertainment, weather information, traffic monitoring, online surveys, and more. However, this open platform also exposes vulnerabilities to malicious users who actively seek to exploit weaknesses in the virtual domain, aiming to gain credentials, financial benefits, or reveal critical information through the use of malware. This constant threat poses a serious challenge in safeguarding sensitive information in cyberspace. To address this challenge, we propose the use of ensemble and deep neural network (DNN) based machine learning (ML) techniques to detect malicious intent packets before they can infiltrate or compromise systems and applications. Attackers employ various tactics to evade existing security systems, such as antivirus or intrusion detection systems, necessitating a robust defense mechanism. Our approach involves implementing an ensemble, a collection of diverse classifiers capable of capturing different attack patterns and better generalizing from highly relevant features, thus enhancing protection against a variety of attacks compared to a single classifier. Given the highly unbalanced dataset, the ensemble classifier effectively addresses this condition, and oversampling is also employed to minimize bias toward the majority class. To prevent overfitting, we utilize Random Forest (RF) and the dropout technique in the DNN. Furthermore, we introduce a DNN to assess its ability to recognize complex attack patterns and variations compared to the ens

关键词： Deep neural networks

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Empowering Low-Resource Language Translation: Methodologies for Bhojpuri-Hindi and Marathi-Hindi ASR and MT 21

Empowering Low-Resource Language Translation: Methodologies ...

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21st International Conference on Spoken Language Translation, IWSLT 2024

作者： Anand, Harpreet Singh Dash, Amulya Ratna Sharma, Yashvardhan Dept. of Computer Science and Information Systems Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798891761414

This paper presents the methodologies implemented for the Automatic Speech Recognition and Machine Translation for the language pairs Bhojpuri-Hindi and Marathi-Hindi for the Dialectal and Low-Resource shared task proposed by The International Conference on Spoken Language Translation (IWSLT) for 2024. The implemented method uses the transcriptions generated through a fine-tuned Whisper models(for Marathi-Hindi) and vakyansh-wav2vec model (for Bhojpuri-Hindi) and generates the translations using fine-tuned NLLB(No Language Left Behind) Models for both the tasks. The selection of more accurate translation is done through sentence-embeddings generated using the MuRIL(Multilingual Representations for Indian Languages)(Khanuja et al., 2021) model for the Marathi-Hindi task. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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A Base Algorithm for Intelligent Traffic Management System for Urban Transportation using 6G Network 18

A Base Algorithm for Intelligent Traffic Management System f...

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18th IEEE International Conference on Advanced Networks and Telecommunications systems, ANTS 2024

作者： Salam, Sameera M. Mitra, Abhinivesh Bits Pilani Hyderabad Campus Dept. of Computer Science and Information Systems Secunderabad India

ISBN: (纸本)9798350391725

Efficient traffic management in urban environments is essential for minimizing congestion and ensuring smooth transportation flow. 6G network is expected to offer faster data speeds, lower latency, and enhanced connectivity. Traditional approaches often struggle to adapt to dynamic traffic patterns and overlook the influence of geographical factors on traffic flow. To address these challenges, this study proposes an innovative approach that integrates gravitational modeling and cluster detection techniques to optimize route planning and congestion management in transportation networks. The shorter response time of the algorithm makes it well-suited for traffic management in a 6G network. This capability allows for real-time data processing and immediate adjustments, enhancing overall traffic flow. The proposed approach utilizes gravitational modeling to simulate traffic interactions between road segments or intersections within the transportation network. By considering factors such as traffic density, user influence, and geographical proximity, the approach aims to identify clusters of interconnected road segments or intersections with similar traffic patterns. These clusters serve as strategic focal points for developing targeted route planning and congestion management strategies. © 2024 IEEE.

关键词： Traffic congestion

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