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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

Neutrosophic Sets and systems 2025年 80卷 728-748页

作者： Fathi, Hanaa Myvizhi, M. Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Eassa, Mohamed Sawah, Mohamed S. Elbehiery, Hussam College of Information Technology Amman Arab University Amman Jordan KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan

This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neutrosophic Graphs (SVHNG) and investigate their characteristics through comprehensive proofs and illustrative examples. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioend Neutrosophic Set Neutrosophic Graph Neutrosophic Sets SVHN-Graph

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Measurement of ChatGPT Performance in Mapping Natural Language Speficaction into an Entity Relationship Diagram 11

Measurement of ChatGPT Performance in Mapping Natural Langua...

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11th IEEE International Conference on systems and Control, ICSC 2023

作者： Omar, Mussa A. University of Ajdabiya Faculty of Information System Computer Science Department Libya

ISBN: (纸本)9798350304886

This paper explores the entity relationship diagram, a popular conceptual model used to depict entities, attributes, and relationships graphically. To help with this, we use ChatGPT, a sophisticated language model based on the GPT architecture, which can translate natural language text into an entity relationship diagram. The paper details the process of evaluating how well ChatGPT can perform compared to other state-of-the-art approaches for entity and relationship extraction. Our experimental findings demonstrate the strong ability of ChatGPT to translate natural language text into entity relationship diagrams, which has potential applications for knowledge graph building, data integration, and database schema design. Moreover, it can aid in automating the extraction and organization of information from unstructured text data, thereby simplifying the study of complex systems. © 2023 IEEE.

关键词： Knowledge graph

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Implementation of Summary-Connection-Prediction (SCP) Instructional Strategy in Flipped Classroom with Moodle: Investigate Preservice Teachers' Acceptance and Beliefs 23

Implementation of Summary-Connection-Prediction (SCP) Instru...

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8th International Conference on Sustainable information Engineering and Technology, SIET 2023

作者： Hariyanti, Uun Batita, Maurish Sofie Rahmi Suharsono, Aswin Department of Information System Faculty of Computer Science Universitas Brawijaya Indonesia

ISBN: (纸本)9798400708503

This study investigated the implementation of summary-connection-prediction (SCP) instructional strategy on students' report tasks before the class under the flipped classroom learning design with Moodle. A total of 106 undergraduate students who enrolled in the Teaching and Learning Theory course participated in this study. A questionnaire based on the technology acceptance model (TAM) and open-ended interview questions were used to collect the data. Following the results, it is known that the preservice teachers have high positive acceptance of SCP strategy. Further, perceived ease of use and perceived usefulness significantly predict behavioral intention, which follows the relationship direction in TAM. Regarding the thematic analysis of interview data, three themes are elaborated to obtain in-depth information about what preservice teachers' beliefs when implementing this strategy. © 2023 ACM.

关键词： Forecasting

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Coexistence of Wi-Fi 6E with LTE-U/LAA in the 5 GHz Frequency Band 8th

Coexistence of Wi-Fi 6E with LTE-U/LAA in the 5 GHz Frequen...

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8th International Congress on information and Communication Technology, ICICT 2023

作者： Ibrahim, Mohamed M. Azzuhri, Saaidal R. Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9789819932351

This work is how to achieve high user throughput with high performance in congested areas by amending the existing coexistence methods and enhancing the performance metrics by updating some input metrics such as duty cycle period, energy detection threshold, data transfer period, etc. The simple topology will verify the basic concept of LTE-U/LAA coexistence with Wi-Fi. Then the indoor-crowded coexistence scenarios of LTE-LAA/U and Wi-Fi 6E in the 5 GHz will be the main idea of this study using the enhanced CAT-4 LBT algorithm design. The proposed model evaluated the coexistence performance based on CDF of throughput and latency. The transport layer of wireless communication will play a worthy role. When using the TCP protocol, the Wi-Fi throughput and latency will be more effective than the LAA operator. When using the UDP protocol, the LTE-LAA network has better throughput and latency than the Wi-Fi network. The throughput and latency for LTE-U and Wi-Fi operators are roughly identical using the FTP protocol. Overall, the throughput improved by 20%, and an approximately 2 ms delay time of the handover compared to the previous research results. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wi-Fi

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Early Parkinson's Disease Detection and Classification using Machine Learning and Neutrosophic Sets

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Neutrosophic Sets and systems 2025年 82卷 873-886页

作者： Oudah, Hasan H. Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed Nagm, Ahmad M. Salama, Ahmed S. College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Engineering and Electronics Cairo Higher Institute for Engineering Computer Science and Management New Cairo Egypt

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification and precise evaluation of the illness's course. However, there is disagreement about the best way to use gait analysis to categorize the severity of motor symptoms and identify early-stage Parkinson's disease. The precision of machine learning (ML) models in identifying early and intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in this study for the prediction of PD. Different metrics are used to evaluate ML models such as accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decision-making methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM methods are used such as CRITIC method to compute the criteria weights and the TOPSIS method to rank the alternatives. These methods are used under the bipolar neutrosophic sets (BNSs) to deal with uncertainty information. The results show the support vector machine is the best ML model for the prediction of PD. © 2025, University of New Mexico. All rights reserved.

关键词： Neurodegenerative diseases

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Blockchain-Driven Secure Data Sharing Framework for Edge Computing Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 978-997页

作者： Fuad A.M.Al-Yarimi Ramzi Salah Khaled Mohamoud Department of Computer Science King Khalid UniversityMuhayel Aseer 62529Saudi Arabia Faculty of Information Science and Technology University Kebangassan MalaysiaBangi 43600Malaysia Department of Information System King Khalid UniversityMuhayel Aseer 62529Saudi Arabia

This study examines secure and effective data sharing methods for edge computing *** methods of sharing data at the edge have issues with security,speed,and *** goal is to develop a Blockchain-based Secure Data Sharing Framework(BSDSF)capable of improving data integrity,latency,and overall network efficiency for edge-cloud computing *** proposes using blockchain technology with Byzantine Fault Tolerance(BFT)and smart contract-based validation as a new method of secure data *** has a two-tiered consensus protocol to meet the needs of edge computing,which requires instantaneous *** employs Byzantine fault tolerance to deal with errors and protect against *** contracts automate validation and consensus operations,while edge computing processes data at the attack *** validation and failure detection methods monitor network quality and dependability,while system security ensures secure communication between *** is an important step toward digital freedom and trust by protecting security and improving transaction *** framework demonstrates a reduction in transaction latency by up to 30％and an increase in throughput by 25％compared to traditional edge computing models,positioning BSDSF as a pivotal solution for fostering digital freedom and trust in edge computing environments.

关键词： Byzantine Fault Tolerance(BFT) blockchain-driven edge computing networks consensus contract data validation contract node validation contract

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Madinah Smart City system: Managing and Monitoring Traffic, Emergency Services, and Crowds in The Haram 10

Madinah Smart City System: Managing and Monitoring Traffic, ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Benaida, Mohamed Islamic University Faculty of Computer Science and Information System Madinah Saudi Arabia

ISBN: (纸本)9789380544472

Billions of Muslims across the globe consider Madinah in Saudi Arabia to be a holy city, thus it attracts millions of visitors every year. Therefore, cases of overcrowding, slow arrival of emergency services, and increased danger to life, can be regular occurrences if not managed efficiently. Consequently, this study aimed to address these abovementioned factors via the utilisation of many tools and mechanisms including cameras, image processing, tracking system, Artificial intelligence hubs, Network Data, Mobile- enabled internet of things sensors etc., as well as data from previous studies and Saudi government agencies. As a result, four main components were realised in the process of creating our proposed framework: control and analysis of traffic signals, control and analysis of human crowding, possessing smart emergency services in real time, and securing data through its collection, storage, and sharing. The combination of all four components fulfils the goals of this research and provides a more robust and efficient environment via improved coordination between different government departments in Madinah, effective emergency services, and improved speed of decision-making, which saves lives in cases of emergency situations where emergency services can attend to the points of concern efficiently and prevents deaths in events of stampedes via the appropriate control and management of crowds. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Data Analytics

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