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Neutrosophic-Supported Machine Learning Models for Oral Disease Classification

Neutrosophic Sets and Systems 2025年 79卷 657-668页

作者： Elezmazy, Ibrahim M. El-Shahat, Doaa Zagazig University Faculty of Computers and Informatics Computer Science Egypt

This study is presented to investigate the influence of the neutrosophic (NS) domain on the performance of the most common machine learning (ML) models. Specifically, it evaluates the effectiveness of Random Forest (RF), Extra Trees (ET), K-Neighbors (KNN), Gaussian Naive Bayes (GaussianNB), and Decision Tree (DT) classifiers in detecting oral diseases. The NS domain divides any image into three membership components: falsity (F), indeterminacy (I) and truth (T), where T denotes the degree to which each pixel in an image is a member of a particular class or category, F denotes the degree to which the pixel is not a member of that class or category and I denotes the degree of uncertainty or indeterminacy. This domain is herein employed to divide the images into three datasets (T, I, and F). Those three datasets, in addition to the original dataset, one by one, are divided into training and testing datasets with a splitting ratio of 80% and 20%, respectively. Afterwards, the five studied ML models are trained on the training dataset before being assessed on the testing dataset to show their ability to generalize using four performance indicators, such as precision, accuracy, F1-score and recall. According to the experimental results, the I-domain, when compared to the other domains, could considerably increase the performance of four of the five studied ML models, meaning that the NS domain might improve the ML models' performance in categorizing oral diseases more correctly. Among all studied models, the RF classifier with the I-domain has the highest classification accuracy, indicating that it is a strong alternative for oral disease classification. © 2025, University of New Mexico. All rights reserved.

关键词： Adversarial machine learning

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Synthesis of batik motifs using a diffusion - generative adversarial network

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Multimedia Tools and Applications 2025年第7期84卷 3407-3438页

作者： Octadion, One Yudistira, Novanto Kurnianingtyas, Diva Informatics Engineering Faculty of Computer Science Universitas Brawijaya Malang Indonesia

This paper presents a novel approach for generating intricate Batik motifs using a modified Diffusion-Generative Adversarial Network (Diffusion-GAN) augmented with StyleGAN2-Ada. Motivated by the rich cultural heritage of Indonesian Batik, our research addresses the challenge of synthesizing high-quality, diverse patterns that capture the artistry and complexity of traditional designs. Traditional generative models often struggle with stability and fidelity in artistic synthesis. We integrate StyleGAN2-Ada and Diffusion techniques to overcome these limitations, optimizing model architecture and employing a curated Batik dataset. Evaluation metrics including Frechet Inception Distance (FID), Kernel Inception Distance (KID), precision, recall, and non-redundancy assess the quality and diversity of generated motifs. Our results demonstrate significant advancements in the realism and authenticity of synthesized Batik patterns, leveraging Diffusion-GAN to enhance detail and artistic fidelity. By exploring the intersection of deep learning and cultural art, this research contributes to the preservation and evolution of Batik as a cultural artifact. References to prior works underscore the evolution of Artificial Intelligence (AI) in cultural heritage preservation, highlighting the potential of modern AI techniques to innovate within traditional art forms like Batik. In summary, this study showcases the efficacy of the proposed Diffusion-GAN methodology in synthesizing original and convincing Batik motifs, while identifying opportunities for further refinement and complexity in motif synthesis. The dataset and code can be accessed here: https://***/octadion/diffusion-stylegan2-ada-pytorch © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Generative adversarial networks

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Proposed Framework for Detection of Breast Tumors

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computers, Materials & Continua 2023年第2期74卷 2927-2944页

作者： Mostafa Elbaz Haitham Elwahsh Ibrahim Mahmoud El-Henawy Department of Computer Science Faculty of Computers and InformaticsKafrelsheikh UniversityKafrelsheikhEgypt Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt

computer vision is one of the significant trends in computer *** plays as a vital role in many applications,especially in the medical *** detection and segmentation of different tumors is a big challenge in the medical *** proposed framework uses ultrasound images from Kaggle,applying five diverse models to denoise the images,using the best possible noise-free image as input to the U-Net model for segmentation of the tumor,and then using the Convolution Neural Network(CNN)model to classify whether the tumor is benign,malignant,or *** main challenge faced by the framework in the segmentation is the speckle ***’s is a multiplicative and negative issue in breast ultrasound imaging,because of this noise,the image resolution and contrast become reduced,which affects the diagnostic value of this imaging *** result,speckle noise reduction is very vital for the segmentation *** framework uses five models such as Generative Adversarial Denoising Network(DGAN-Net),Denoising U-Shaped Net(D-U-NET),Batch Renormalization U-Net(Br-UNET),Generative Adversarial Network(GAN),and Nonlocal Neutrosophic ofWiener Filtering(NLNWF)for reducing the speckle noise from the breast ultrasound images then choose the best image according to peak signal to noise ratio(PSNR)for each level of *** five used methods have been compared with classical filters such as Bilateral,Frost,Kuan,and Lee and they proved their efficiency according to PSNR in different levels of *** five diverse models are achieved PSNR results for speckle noise at level(0.1,0.25,0.5,0.75),(33.354,29.415,27.218,24.115),(31.424,28.353,27.246,24.244),(32.243,28.42,27.744,24.893),(31.234,28.212,26.983,23.234)and(33.013,29.491,28.556,25.011)forDGAN,Br-U-NET,D-U-NET,GANand NLNWF *** to the value of PSNR and level of speckle noise,the best image passed for segmentation using U-Net and classification usingCNNto detect tumor *** experiments proved

关键词： Breast tumor speckle noise GAN model U-Net model neutrosophic

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Advanced deep learning models based on neutrosophic logic for the analysis of brain tumor medical images

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Neutrosophic Sets and Systems 2025年 82卷 924-939页

作者： Atef, Dina El Shahat, Doaa Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

The categorization of medical photographs poses considerable difficulties owing to noise, uncertainty, and ambiguous information. Conventional deep learning models frequently encounter difficulties in addressing this issue, resulting in diminished diagnostic precision, particularly in the context of low-quality or ambiguous situations. This work presents a hybrid methodology that combines Neutrosophic Set (NS) theory with deep learning models to improve magnetic resonance imaging (MRI) picture classification in uncertain settings. NS theory delineates three domains: True (T), Indeterminate (I) and False (F) to address picture uncertainty and noise, hence enhancing deep learning models' capacity to analyze complex, ambiguous visual data. To assess the methodology, four advanced deep learning models MobileNet, VGG16, DenseNet121 and InceptionV3 were employed, and their efficacy was analyzed on brain tumor medical image datasets. The findings demonstrate that models trained on NS-transformed data, especially DenseNet and inception, produce enhanced results relative to those trained on the original data, attaining notably higher accuracy, precision, and recall. This illustrates that integrating NS theory into deep learning models markedly improves their capacity to categorize uncertain and noisy MRI pictures, offering a reliable method for enhancing diagnostic accuracy in medical imaging. © 2025, University of New Mexico. All rights reserved.

关键词： Convolutional neural networks

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Enhancing demand forecasting through combination of anomaly detection and continuous improvement

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Soft Computing 2025年第2期29卷 1243-1258页

作者： Jahani, Meysam Zojaji, Zahra Raji, Fatemeh Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Demand forecasting has emerged as a crucial element in supply chain management. It is essential to identify anomalous data and continuously improve the forecasting model with new data. However, existing literature fails to comprehensively cover both aspects of anomaly detection and continuous improvement in demand forecasting. This study proposes an enhanced model to improve accuracy in the demand forecasting. The proposed model introduces a novel data handling method that incorporates an anomaly detection autoencoder, improved with anomaly correction mechanisms. The data handling approach simultaneously detects data anomalies, distinguishes between expected and unexpected anomalies, and corrects anomalous data, ensuring cleaner input for demand forecasting. Then, the proposed model employs a long short-term memory architecture for demand forecasting, enhanced with a continuous improvement method. Thus, the model not only forecasts demand but also retrains the model when the anomaly data surpasses the predetermined threshold, thereby improving the accuracy of forecasting. The results show that the proposed model outperforms other models in detecting data anomalies, achieving an average precision-recall of 0.922, a receiver operating characteristic value of 0.739, and a significance level of less than 0.05. Finally, the model exhibits superior performance in demand forecasting, with average mean squared error, root mean squared error, and mean absolute error values of 33.167, 4.347, and 1.509, respectively, all with a significance level of less than 0.05. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Demand response

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Advanced Cross-Language Clone Detection Using Modified AST and Graph Neural Network

Advanced Cross-Language Clone Detection Using Modified AST a...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Swilam, Zeina Hamdy, Abeer Pester, Andreas Faculty of Informatics & Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Cross-language code clone detection is a critical issue in software engineering, driven by the increasing use of multi-language systems within large-scale projects. Code clones-fragments of code with similar functionality but varied implementations-complicate software maintenance, elevating the risk of defects and increasing the cost of debugging and refactoring. Traditional Abstract Syntax Trees (ASTs) are widely used for clone detection but are often insufficient for cross-language scenarios due to their limitations in capturing comprehensive structural and semantic information. To overcome these limitations, this research introduces a modified AST, enhanced by the inclusion of Condition-Type Edges, which effectively model logical dependencies within control structures. This novel extension offers a deeper semantic understanding of the code's decision-making processes, addressing the shortcomings of conventional ASTs that focus solely on syntactic relationships. By integrating this modified AST with Graph Neural Networks (GNNs), our approach learns robust feature representations that capture semantic and structural variations across programming languages. Experimental evaluations on Java-Python code pairs show a notable improvement in detection accuracy, achieving a recall of 0.96, precision of 0.88, and an F1 score of 0.92, outperforming state-of-the-art techniques. These findings underscore the value of Condition-Type Edges in enhancing AST representations, providing a more reliable framework for cross-language code clone detection, and ultimately improving software quality and maintenance in multi-language environments. © 2024 IEEE.

关键词： Program debugging

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MTR Model-Based Testing Framework

Infocommunications Journal

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Infocommunications Journal 2024年第2期16卷 11-18页

作者： Németh, Gábor Árpád Lugosi, Máté István The Department of Computer Algebra Faculty of Informatics Eötvös Loránd University Budapest Hungary

In this article we propose a novel, free and open-source model-based testing framework for finite state machine specifications. The various model handling and test generation options make the framework suitable for testing complex systems and provide a solid background for investigating different automated test design methodologies. The complexity and fault detection capabilities of the available algorithms are investigated through analytical analyses and simulations applying randomly injected faults. © 2024 Scientific Association for Infocommunications. All rights reserved.

关键词： Model checking

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Hybrid zk-STARK and zk-SNARK Framework for Privacy-Preserving Smart Contract Data Feeds

Hybrid zk-STARK and zk-SNARK Framework for Privacy-Preservin...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Nassar, Seif T. Hamdy, Abeer Nagaty, Khaled Department of Computer Networks Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350367560

Decentralized applications (DApps) are increasingly using off-chain data, yet growing concerns about data privacy hinder their widespread adoption. Zero-knowledge proofs (ZKPs) have emerged as a solution to this problem. This paper proposes a novel hybrid framework that combines Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs) and Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) to deliver an efficient, scalable, quantum-resistant, and privacy-preserving ZKP system. The framework employs zk-STARKs' ability to handle large computations with quantum resistance and zk-SNARKs' succinct proofs and fast verification. The hybrid framework utilizes advanced arithmetization techniques to balance scalability, privacy, and security, including Algebraic Intermediate Representation (AIR) for zk-STARKs and Rank-1 Constraint Systems (RlCS) or PLONKish constraints for zk-SNARKs. The framework uses the Kate-Zaverucha-Goldberg (KZG) polynomial commitment scheme for reliability and transparency and eliminates trusted setup by using Discrete-logarithm-based Argument of Recursive Knowledge (DARK) commitments and Poseidon hashing. This paper details the construction of the hybrid proof system, analyzes its complexity, and explores its potential applications. The framework solves smart contract data feed limitations, security, efficiency, and scalability while prioritizing privacy. © 2024 IEEE.

关键词： Differential privacy

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Modeling Parking Occupancy Using Algorithm of 3D Visibility Network

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IEEE Access 2025年 13卷 84424-84434页

作者： Babič, Matej Šter, Branko Hluchý, Ladislav Baiamonte, Giuliana Calì, Michele Faculty of Information Studies Novo Mesto8000 Slovenia University of Ljubljana Faculty of Computer and Informatics Ljubljana1000 Slovenia Institute of Informatics Slovak Academy of Sciences Bratislava845 07 Slovakia University of Catania Electric Electronics and Computer Engineering Department Catania95125 Italy

The modernization of the information communication infrastructure of the regional data transmission network has advanced in order to increase the maximum transmission speed of existing transport routes, ensuring the quality of the service and its reliability. In the present research work, a new approach for the 3D visibility network algorithm has been developed, showing how graph theory applied to wireless sensor networks (WSNs) enables technological development for new solutions in areas such as public infrastructure. The possibility of determining in such networks whether an area of interest is sufficiently covered by a given set of sensors by means of the Voronoi diagram is discussed. The parking dynamics and parking system were modeled with cellular neural networks (CNNs) based on weather conditions, and magnetic parking sensors were replaced with pillar sensors. The proposed method has proven its effectiveness in determining the position of the minimum sensors covering the area of interest, in order to find a solution in the occupation of parking spaces in the presence of different weather conditions. The proposed approach and experimental results offer potential applications in various fields such as lighting and rendering, motion planning, pattern recognition, computer graphics and computational geometry, in order to conduct studies on problems and perspectives of pillar sensor technology while reducing costs compared to magnetic ones. © 2013 IEEE.

关键词： Rendering (computer graphics)

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Navigating Autism: The Role of Collaborative Virtual Reality in Social Skills Development 17

Navigating Autism: The Role of Collaborative Virtual Reality...

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17th International Conference on computer Supported Education, CSEDU 2025

作者： Sharaf, Nada Faculty of Informatics and Computer Science The German International University Egypt

ISBN: (纸本)9789897587467

Autism Spectrum Disorders (ASD) require new interventions because social communication can be specially challenging. Such interventions could help social skills grow. Collaborative virtual reality (VR) technology enables having different interactive and engaging environments. It offers a promising ways for improving many skills since people can safely engage with others as well as learn with others. This paper reviews the application of collaborative VR in improving social skills among children with ASD. The reviews shows the potential of collaborative VR to simulate deeply dynamic social interactions in a carefully controlled setting as well. Different studies show that collaborative VR use can help children with ASD improve empathy, social understanding, and teamwork. However, there are still some difficulties including accessibility as well as the possible need for individualized interventions and potential sensory overload. This review points out the opportunities that collaborative VR provides for meaningful learning improvements and for the large practice of social interactions in an engaging way. It also discusses the actual obstacles,that need to be addressed, to completely maximize the advantages of VR technologies regarding social skills development in ASD. The provided analysis could help support innovation and research in the field. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Engineering education

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