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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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Advanced Machine Learning Approaches for Breast Cancer Detection with Neutrosophic Sets

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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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MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

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Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： computer aided analysis

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary information

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Advances in Atmospheric Sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer 2

Deep Learning and Machine Learning Algorithms to Predict Lun...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Gowthami, K. Kamalakannan, J. School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Diseases

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Investigation of the Features of Ricocheting Ionospheric Propagation of Decameter Radio Waves

Investigation of the Features of Ricocheting Ionospheric Pro...

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2024 systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2024

作者： Kryukovsky, A.S. Mikhaleva, E.V. Lukin, D.S. Rastyagaev, D.V. Institute of Information System and Engineer - Computer Technology Russian New University Moscow Russia

ISBN: (纸本)9798350383614

The ray and caustic features of the ricocheting propagation of decameter radio waves in the Earth's ionosphere have been studied. The rays and caustic structures of radio waves during trans-ionospheric channeled propagation of radiation from an elevated source are calculated using the bicharacteristics method. Ray oscillations arising during circumnavigation of the world have been studied. Using the extended bicharacteristic system method, the Jacobian of the ray flux divergence and, on its basis, the attenuation of the radio signal are calculated. ©2024 IEEE.

关键词： Radio waves

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The Impact of information system and technology of Courier Service During Pandemic 7th

The Impact of Information System and Technology of Courier S...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Siahaan, Andre Timiko Pinajung, Bagas Rizkyka Kennidy Gaol, Ford Lumban Matsuo, Tokuro School of Information System Bina Nusantara University Alam Sutra Tangerang13960 Indonesia Computer Science Department BINUS Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819951659

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： information systems

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Predictive Analytics in Mobile Education: Evaluating Logistic Regression, Random Forest, and Gradient Boosting for Course Completion Forecasting

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 210-232页

作者： Hayadi, B. Herawan Hariguna, Taqwa Information Technology Education Faculty of Teacher Training and Education Bina Bangsa University Banten 42124 Indonesia Department of Information System and Magister Computer Sciences Universitas Amikom Purwokerto Purwokerto Utara 53127 Indonesia

This study aimed to compare the effectiveness of three predictive algorithms—logistic regression, random forest, and GBM—in predicting course completion using user engagement data from online learning platforms. By analyzing engagement metrics such as session duration, session frequency, and quiz scores, the study sought to identify the most effective model for forecasting course completion, providing insights into which aspects of student behavior were most predictive of success. Logistic regression emerged as the best overall performer, achieving the highest accuracy (52.13%) and F1-Score (56.17%), indicating its balanced approach to predicting course completion and non-completion. Random forest and gradient boosting machines (GBM) showed strengths in specific areas;random forest maintained a good balance between precision and recall, while GBM excelled in recall, identifying students likely to complete courses but with lower precision, leading to more false positives. The findings have practical implications for educational technology, particularly in designing personalized learning paths and targeted interventions to support at-risk students. The study also acknowledged limitations, including the dataset’s focus on engagement metrics without demographic context and the potential for model-specific biases. Future research should explore additional predictive features, larger datasets, and more advanced algorithms to enhance the robustness and applicability of predictive models in real-time educational settings. © 2025 by the authors of this article.

关键词： Prediction models

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Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone Sensors

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IEEE Access 2024年 12卷 514-533页

作者： Taherinavid, Siavash Moravvej, Seyed Vahid Chen, Yen-Lin Yang, Jing Ku, Chin Soon Yee, Por Lip Iran University of Science and Technology School of Civil Engineering Tehran13114-16846 Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve

关键词： Smartphones

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Preventing 51% Attack by Using Consecutive Block Limits in Bitcoin

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IEEE Access 2024年 12卷 77852-77869页

作者： Mahmood Babur, Sohail Ur Rehman Khan, Shafiq Yang, Jing Chen, Yen-Lin Soon Ku, Chin Yee Por, Lip Government Murray College Department of Information Technology Sialkot51010 Pakistan Capital University of Science and Technology Department of Computer Science Islamabad44730 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

In permissionless blockchain systems, Proof of Work (PoW) is utilized to address the issues of double-spending and transaction starvation. When an attacker acquires more than 50% of the hash power of the entire network, they gain the ability to engage in double-spending activities, posing a significant threat to the PoW consensus algorithm. This research focuses on the consensus algorithm employed in the Bitcoin system, explaining how it operates and the security challenges it faces. The proposed modification to the PoW algorithm imposes a restriction on miners: they are not allowed to accept consecutive blocks from the same miner into the final local blockchain to prevent the 51% attack problem. This modification supports transactions that require six confirmations. In the event an attacker attempts a 51% attack with a private chain that consists of fewer than 6 blocks, it becomes easier to detect a double-spending attack before accepting the attacker's private chain. The modified algorithm introduces a 'Safe Mode Detection Algorithm' that scrutinizes incoming blocks for adjustments at the top of the local blockchain. If inconsistencies are identified, the consensus algorithm proceeds cautiously by comparing the UTXO dictionaries from the attacker's chain with those from the miner's own blockchain. This meticulous comparison aims to detect instances of double-spending. If such instances are detected, the miner rejects the attacker's chain, establishing a double-spend-free environment and thwarting 51% attacks. © 2013 IEEE.

关键词： Blockchain

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