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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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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

引用

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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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture 7

Augmented Karuta: Interactive Playing Cards on the Floor for...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Umezu, Nobuyuki Bando, Hideki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798331521578

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 'grabbing' cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding 'reading' card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected 'grabbing' card that matches the 'reading' card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness. © 2025 IEEE.

关键词： Tropics

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Breaking Tester Stereotypes: who is testing and why it matters 24

Breaking Tester Stereotypes: who is testing and why it matte...

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Proceedings of the 37th International BCS Human-computer Interaction Conference

作者： Isabel Evans Chris Porter Mark Micallef Dept. of Computer Information Systems Faculty of ICT University of Malta Msida Malta Dept. of Computer Science

Testing is essential for successful delivery of software solutions and is not always performed by specialist testers. In earlier studies, we noted a wide diversity in the backgrounds of the testers participating in these studies, prompting our research question: Who is Testing? We conducted a qualitative study of over 70 industry testers, covering testers from multiple countries and domains, with information about their backgrounds, hobbies, roles and characteristics. We show testers are from a wide range of backgrounds, with differing needs, characteristics, and problem-solving preferences. Their roles in software projects are multi-faceted, requiring a high cognitive skill level. We discuss how to break stereotyping and best support diversity in testers’ backgrounds and *** consider whether software testers are different from other software practitioners, and how understanding tester personas helps support of testers and testing.

关键词： Software Testing

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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

引用

Power Electronics, computer Applications (ICPECA), IEEE International Conference on

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

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

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.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems Real-time systems Gas detectors Resilience

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Enhancing Train Transportation in Sri Lanka: A Smart IOT based Multi-Subsystem Approach using MQTT

arXiv

引用

arXiv 2025年

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

This research proposes a system as a solution for the challenges faced by Sri Lanka’s historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study’s data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka’s railway infrastructure. © 2025, CC BY.

关键词： Global positioning system

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Predictions of Gender from the Analysis of the Images of Iris and Ocular Regions

Predictions of Gender from the Analysis of the Images of Iri...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Masoud Husayn Mohamed A.E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

the investigation of biological measurements has become a powerful for classifications. The analysis of eye images for gender classification is proven that it is one of the reliable form identification. This paper examines use of support vector machines and CNN for gender predictions based on analyzing images of the ocular and iris regions for determining which one provides more accurate results. Feature extracted by HOG and LBP methods are examined by the SVM classifier. Whilst, pertained CNN is examined with considering the architecture of AlexNet and ResNet18. The proposed models are then evaluated on the MMU2 dataset which contains 995 images. The results of CNN with AlexNet has shown that the eye region yields an accuracy rate of 94.19% higher than the iris with an accuracy of 90.46%. This indicates the feasibility for applications in security, forensics, marketing, customer service improvement.

关键词： Support vector machines Biometrics Iris Accuracy Image analysis Forensics Machine learning Feature extraction Security Reliability

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Detection of Upper Limbs Fractures from X-Ray Images Using Machine Learning Techniques

Detection of Upper Limbs Fractures from X-Ray Images Using M...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdalkreem Masaod Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Bone fractures are common injuries which might affect individuals at any age. The diagnosis should be accurate for proper treatment. Although conventional X-ray imaging is generally practiced, physicians might not be able to diagnose the fractures due several challenges such as infinitesimal fractures that could lead to the misdiagnosis. The misdiagnosis is probably happened especially in the case of the small size of bones. This research is carried out to improve detection of upper limbs fractures. In each upper limbs there are 32 bones. 27 bones are in balms which are quite small pieces. Therefore, an accurate diagnosis is really requires. Automatic diagnosis of bone fractures has already been exploited. However, there are several issues due to the accuracy rate. The proposed framework in this study is an experiment to obtain high level of results. It involves: data gathering, preparation, feature extraction, selection of the best features, and classification. It employs advanced methods such as Gray Level Co-occurrence Matrix, Local Binary Patterns, and deep learning models like VGG16 and DenseNet to improve the diagnosis accuracy. The findings have shown that the SVM classifier yields a rate accuracy of 96%, which outperforms other methods. These findings have been yielded from analyzing the dataset of 8942 samples. This result shows that the proposed framework has outperform other studies.

关键词： Support vector machines Deep learning Limbs Accuracy Medical services Bones Feature extraction X-ray imaging Injuries Computational intelligence

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Leveraging the Machine Learning Techniques for Diagnosing Liver Diseases

Leveraging the Machine Learning Techniques for Diagnosing Li...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Warda A. A. Altawati Lubna A. B. Mohammed Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Liver diseases are known as one of the deadly diseases around the world. The diagnosis is crucial due to the fact of that the high incidence and death rates. Although, there are commonly used methods for diagnosing liver disorders, they are too expensive. People who are at risk of liver health issues would benefit from the early and low-cost diagnosis for effective treatment. Recently, machine learning plays obvious roles on healthcare to help medical experts to detect illnesses at early onset of the disease. The diagnosis of the liver diseases has been considered in several studies. This paper develops the accuracy rate of the diagnosis of liver diseases with less size of features. Decreasing the size of feature vectors minimizes the time cost of the machine learning model. The paper puts into practice feature selection techniques: Relief, PCA, PSO, and OS to gain its aim. The proposed method of this paper of the diagnosis is applied on two different datasets: ILPD and BUPA. Four different experiments have been carried out for acquiring high level of outcomes. Despite the promised results obtained from all feature selection techniques considered in this study, the selected features using Relief with the random forest classifier (RF) has yielded the best accuracy rates of 91.3% and 98.55% from ILPD and BUPA respectively.

关键词： Accuracy Liver diseases Mortality Feature extraction Vectors Particle swarm optimization Random forests Principal component analysis Diseases Classification tree analysis

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Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

arXiv

引用

arXiv 2025年

作者： Agheli, Pouya Pappas, Nikolaos Kountouris, Marios Communication Systems Dept. EURECOM France Dept. of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Computer and Information Science Linköping University Sweden

This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce a grade of effectiveness (GoE) metric. Furthermore, we integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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