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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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A biological-like synthesis framework for software engineering environments

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International Journal of computers and Applications 2024年第4期46卷 208-217页

作者： Sophatsathit, Peraphon Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

This research proposes a Biological-like Architecture for Software Systems (BASS) that make up of software components. The design principle is to mimic the simplicity of uni-cellular life form as fixed-sized blocks linearly arranged as a flat array so that all references to those components can be reached directly. Each component holds its attributes and operations to permit self-execution without external support. Like living cells, this autonomy is modeled to be short-lived that undergoes a three-stage life cycle, namely, creation, sustainment, and cessation. This cycle must run to completion within a predefined Time-To-Live (TTL) limit. When the time limit of an existing component expires, a new component is created to replace in situ to conserve system energy and overhead. A simulation is conducted to test the operating characteristics of this three-stage life cycle under the time limit. The results reveal that component replacement in situ is practically viable. It is envisioned that BASS will offer an energy-efficient environment that can alleviate software systems resource utilization considerably. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Life cycle

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Anomaly Detection Control Through One-class Learning in Defect Inspection Systems

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Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 328-333页

作者： Jeon, Janggun Kim, Namgi Dept of AI Computer Engineering and Dept of computer science Kyonggi University Korea Republic of

This paper presents a novel image-level anomaly detection control method for defect inspection, which comprises featureextraction from high-dimensional data and feature-learning to analyze the observed patterns. we focus on the feature-learning step, especially in Deep SVDD. To determine anomalies from images of object suspected to be defective, Deep SVDD learns the decision boundaries for the normal feature distribution in the latent space. When a new image is received by the defect inspection system, Deep SVDD maps its features to the latent space and detects anomalies based on whether the observed features fall within the decision boundaries for normal features. The decision boundary of Deep SVDD is determined based on the center-point, which is the center position of the decision boundary. The center-point is randomly fixed at the beginning of training of Deep SVDD. However, this limits the possibility of finding the optimal decision boundary. Therefore, we propose a center-point-moving method to optimize the center-point location and improve the performance of Deep SVDD. To confirm the suitability of the proposed method, we conducted experiments on MVTec-AD, a defect inspection dataset for several products in the manufacturing industry. Based on the results, the proposed method improved the performance of Deep SVDD in 11 of the 15 class domains in the MVTec-AD dataset. © ICROS 2025.

关键词： Inspection equipment

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A Novel Certificateless Signature-based Access Control Scheme for Named In-network Computing Service

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 38-45页

作者： Li, Wanji Liu, Qiangbin Zhu, Yi School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China School of Computer Science and Communication Engineering the Dean of Department of Communication Engineering Jiangsu University Zhenjiang China

Named in-network computing service (NICS) is a potential computing paradigm emerged recently. Benefitted from the characteristics of named addressing and routing, NICS can be flexibly deployed on NDN router side and provide nearby computing service to Internet users. But the NICS feature of dynamic deployment also causes serious security risk of access control. How to independently check out the subscription relationship between requester and requested computing service on NICS side become a challenge. To solve this problem, we propose a novel certificateless signature-based access control scheme (CS-ACS) in this paper. In CS-ACS, the entire user public-private key pairs consist of two parts, user side and source server side. Where, the user public-private key pairs (server side) are generated according to the user ID, service subscription relationship and subscription expiration time. Based on this special design, when authorized user signs the interest packet of invoking specific service using its private key, the NICS can verify the signature then check out whether the requester is a valid subscriber and the subscription is expired or not. Simulation results show that, comparing with fundamental solutions, CS-ACS can avoid extra secret key storage cost on NICS side and markedly shorten authentication delay. © (2025), (International Association of Engineers). All rights reserved.

关键词： Authentication

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Secure kNN For Distributed Cloud Environment Using Fully Homomorphic Encryption

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IEEE Transactions on Cloud Computing 2025年第2期13卷 721-736页

作者： Fukuchi, Yuuya Hashimoto, Sota Sakai, Kazuya Fukumoto, Satoshi Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Auburn University Department of Computer Science and Software Engineering AuburnAL United States

Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this paper, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost. © 2013 IEEE.

关键词： Differential privacy

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Understanding Government Enterprise Architecture: A Review and Case Study

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IEEE Access 2025年 13卷 90088-90108页

作者： Trimanadi, Raden Indra Sensuse, Dana University of Indonesia Faculty of Computer Science Depok16424 Indonesia

The implementation of enterprise architecture (EA) is no longer exclusive to large corporations;its principles have been adapted for various organizations, including small and medium-sized enterprises (SMEs) and industry-specific entities. Recognizing its potential, many governments have also adopted EA frameworks to address growing complexities in public sector operations. This adaptation, termed Government Enterprise Architecture (GEA), aims to enhance governance efficiency and digital transformation. However, GEA implementation remains a work in progress, with significant gaps in standardization and execution. For instance, Indonesia introduced GEA in 2018, but many implementers struggled with misinterpretations of the framework's objectives and processes. To address these challenges, this study conducts a comprehensive global comparison of GEA methodologies. Using a Systematic Literature Review (SLR), the research examines key aspects of GEA, including its core principles, architectural domains, critical success factors, performance metrics, and potential benefits. By analyzing these dimensions, the study provides actionable insights for GEA developers - particularly in Indonesia, where the initiative is still nascent. A deeper understanding of global best practices can help refine Indonesia's approach, ensuring effective implementation and maximizing GEA's value in improving public sector governance. © 2013 IEEE.

关键词： Electronic voting

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Self-supervised Hypergraph Transformer with Alignment and Uniformity for Recommendation

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 292-300页

作者： Yang, XianFeng Liu, Yang School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of computer Science and Software Engineering University of Science and Technology Liaoning Anshan China

Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets and neglected the alignment and uniformity of embedding representation. The noisy and skewed distribution of data from real-world applications is ubiquitous. The alignment and uniformity are crucial for representation learning. In this paper, we propose SHTAU (self-supervised hypergraph transformer with alignment and uniformity for the recommendation) to address the problem. Specifically, we first perform a graph neural network on the user-item interaction graph, then captures the global collaboration relationship between users and items through the hypergraph transformer structure, and use the extracted global information to generate self-supervised signals for data enhancement on the user-item interaction graph to enhance the robustness of the recommendation algorithm. Simultaneously, alignment and uniformity auxiliary task update embeddings from different angles to improve performance. Extensive experiments are conducted on Yelp, Gowalla, MovieLens, Amazon-book and Tmall datasets. The experimental results show that SHTAU has obvious advantages over the baseline methods. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Alignment

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Empirical Evaluation of Algorithm Performance: Addressing Execution Time Measurement Challenges

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Informatica (Slovenia) 2024年第4期48卷 543-548页

作者： Dobravec, Tomaž Faculty of Computer and Information Science University of Ljubljana Slovenia

In this paper, we investigate the influence of various factors, such as programming language, testing environment, and input data, on the accuracy of algorithm execution measurements. To conduct this study, we used the BubbleSort algorithm as a test case and implemented it in Java, C, and x86 assembly languages. We executed these implementations with various inputs and performed an empirical evaluation of the results using the ALGator system. We showed that the influence of the chosen programming language is negligible, since the Java and C implementations gave very similar results, while the assembler implementation differed only by a constant factor. Furthermore, our analysis emphasized the importance of repeating tests to obtain precise timing measurements - the more tests we do, the more accurate the measured result will be. We also discuss the impact of the input data type which can significantly affect the execution time due to the increased number of mispredictions of the branch predictor. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Time measurement

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Deepfake Audio Detection for Urdu Language Using Deep Neural Networks

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IEEE Access 2025年 13卷 97765-97778页

作者： Ahmad, Omair Khan, Muhammad Sohail Jan, Salman Khan, Inayat University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Arab Open University Faculty of Computer Studies A’Ali732 Bahrain University of Engineering and Technology Department of Computer Science Mardan Pakistan

Audio Deepfakes, which are highly realistic fake audio recordings driven by AI tools that clone human voices, With Advancements in Text-Based Speech Generation (TTS) and Vocal Conversion (VC) technologies have enabled it easier to create realistic synthetic and imitative speech, making audio Deepfakes a common and potentially dangerous form of deception. Well-known people, like politicians and celebrities, are often targeted. They get tricked into saying controversial things in fake recordings, causing trouble on social media. Even kids’ voices are cloned to scam parents into ransom payments, etc. Therefore, developing effective algorithms to distinguish Deepfake audio from real audio is critical to preventing such frauds. Various Machine learning (ML) and Deep learning (DL) techniques have been created to identify audio Deepfakes. However, most of these solutions are trained on datasets in English, Portuguese, French, and Spanish, expressing concerns regarding their correctness for other languages. The main goal of the research presented in this paper is to evaluate the effectiveness of deep learning neural networks in detecting audio Deepfakes in the Urdu language. Since there’s no suitable dataset of Urdu audio available for this purpose, we created our own dataset (URFV) utilizing both genuine and fake audio recordings. The Urdu Original/real audio recordings were gathered from random youtube podcasts and generated as Deepfake audios using the RVC model. Our dataset has three versions with clips of 5, 10, and 15 seconds. We have built various deep learning neural networks like (RNN+LSTM, CNN+attention, TCN, CNN+RNN) to detect Deepfake audio made through imitation or synthetic techniques. The proposed approach extracts Mel-Frequency-Cepstral-Coefficients (MFCC) features from the audios in the dataset. When tested and evaluated, Our models’ accuracy across datasets was noteworthy. 97.78% (5s), 98.89% (10s), and 98.33% (15s) were remarkable results for the RNN+LSTM

关键词： Deep neural networks

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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