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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

Neutrosophic Sets and systems 2025年 81卷 273-284页

作者： Elbehiery, Hussam Fathi, Hanaa Eassa, Mohamed Abdelhafeez, Ahmed Abdellah, Mohamed Refaat Mahmoud, Hadeer Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt College of Information Technology Amman Arab University Amman Jordan 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 Artificial Intelligence Faculty of Computers and Artificial Intelligence Modern University for Technology & Information Cairo Egypt

Breast Cancer (BC) remains a significant health challenge for women and is one of the leading causes of mortality worldwide. Accurate diagnosis is critical for successful therapy and increased survival rates. Recent advances in medical imaging and computational technologies have enabled more precise methods of detecting and evaluating breast cancer. Accurate analysis and diagnosis utilizing medical imaging have developed as essential research topics, providing important help in clinical decision-making for various illnesses, including breast cancer. Machine learning (ML) can accurately predict breast cancer. But the breast cancer data has vague and uncertainty information. So, the neutrosophic sets (NSs) are used in this study to deal with uncertainty data. We convert the original dataset into neutrosophic data with three components such as truth, indeterminacy, and falsity values. Then we applied four ML models with N-data such as logistic regression, gradient boosting (GB), k-nearest neighbor (KNN), and support vector machines (SVM), to improve diagnostic accuracy. Then we compared the ML models with and without using N-data. The results show the logistic regression has higher accuracy with 98.6% with the N-data and 95.80% without N-data. So, the NSs can improve the accuracy of ML models. © 2025, University of New Mexico. All rights reserved.

关键词： Lung cancer

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Single-Valued Neutrosophic Graph with Heptapartitioend Structure

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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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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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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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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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A novel U-net model for brain tumor segmentation from MRI images

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Alexandria Engineering Journal 2025年 126卷 220-230页

作者： Obayya, Marwa Alshuhail, Asma Mahmood, Khalid Alanazi, Meshari H. Alqahtani, Mohammed Aljehane, Nojood O. Almansour, Hamad Al-Hagery, Mohammed Abdullah Department of Biomedical Engineering College of Engineering Princess Nourah bint Abdulrahman University PO Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal university Saudi Arabia Department of Information Systems Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science College of Sciences Northern Border University Arar Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Applied Collage Najran University Najran Saudi Arabia Department of Computer Science College of Computer Qassim University Saudi Arabia

Segmentation of brain tumors aids in diagnosing the disease early, planning treatment, and monitoring its progression in medical image analysis. Automation is necessary to eliminate the time and variability associated with traditional segmentation methods. Convolutional neural networks (CNNs) and U-Net architectures have demonstrated their efficiency and effectiveness in segmenting brain tumors from MRI images using deep learning techniques. The paper presents an improved U-Net-based segmentation algorithm that integrates nested skip paths to improve encoder-decoder feature fusion. The performance of segmentation was optimized by utilizing a variety of activation functions and loss functions, including Dice Loss and Intersection over Union (IoU). A high level of accuracy was demonstrated in the proposed model when it was evaluated using the LGG Segmentation Dataset. The proposed approach for segmenting medical images has been shown to be both robust and efficient in a comparative analysis. © 2025 The Authors

关键词： Convolutional neural networks

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Cocktail Party Effect Using Parallel Intra and Inter Self-attention 3rd

Cocktail Party Effect Using Parallel Intra and Inter Self-at...

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3rd International Conference on Advances in information and Communication technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq 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 Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Revolutionizing healthcare: An IoT-driven approach to remote patient health monitoring and management during the pandemic and beyond

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Alexandria Engineering Journal 2025年 126卷 358-367页

作者： Al-Wesabi, Fahd N. Elhessewi, Ghada Moh. Samir Alqahtani, Mohammed Alshuhail, Asma Althobaiti, Turke Aljehane, Nojood O. Alzahrani, Mohammed Yahya Almansour, Hamad Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Health Sciences College of Health and Rehabilitation Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Department of Computer Science Faculty of Science Northern Border University Arar73222 Saudi Arabia Department of Computer Science Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Saudi Electronic University Saudi Arabia Applied Collage Najran University Najran Saudi Arabia

The integration of Internet of Things (IoT) technologies into the healthcare sector has witnessed substantial growth, particularly during global health emergencies such as the COVID-19 pandemic. This research introduces a Smart Health Monitoring system (SHMS) that enables real-time patient health tracking and environmental monitoring using IoT and machine learning (ML). The primary goal of the system is to provide continuous and remote monitoring of vital signs, including Body Temperature (BT), Heart Rate (HR), Environmental Temperature (ET), and Environmental Humidity (EH). Sensor-based data acquisition is managed through microcontroller hardware, with data transmitted via Wi-Fi to cloud-based platforms accessible through smartphones, laptops, and other internet-enabled devices. The system processes data using machine learning models—Logistic Regression and Decision Tree—to predict the patient's health status with high accuracy. Results indicate that the Logistic Regression model outperforms the Decision Tree model, achieving an accuracy rate of 88.89 %. Additionally, the SHMS architecture is scalable, supporting multi-user environments and large numbers of connected devices, making it suitable for real-world healthcare deployment. To enhance pandemic responsiveness, the system can be extended to measure additional parameters such as Respiratory Rate (RR) and Oxygen Saturation (SpO₂), which are essential during outbreaks of respiratory diseases like COVID-19 and influenza. This solution offers an effective and accessible tool for early detection, timely intervention, and reduced burden on healthcare infrastructure, especially during health crises. © 2025 The Authors

关键词： mHealth

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators 27

Studying of Machine Learning Models for Forecasting Macroeco...

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27th International Conference on Advanced Communications technology, ICACT 2025

作者： Ngoc, Lan Dong Thi Ngo-Thi-Thu-Trang Kim, Kyoung-Mok Pham, Vung Nguyen, Ha-Nam Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi122300 Viet Nam Computer Science Department Sam Houston State University HuntsvilleTX77340 United States Department of Medical Information Sahmyook Health University Seoul Korea Republic of Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (纸本)9791188428137

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R2 metrics to evaluate the accuracy of the forecasts. The experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful tuning. This study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Inflation

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