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A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems

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International Journal of Engineering, Transactions B: Applications 2025年第11期38卷 2657-2674页

作者： Safarpour, M.H. Alavi, S.M. Izadikhah, M. Dibachi, H. Department of Mathematics and Computer Science Arak Branch Islamic Azad University Arak Iran

Considering the high volume, wide variety, and rapid speed of data generation, investigating feature selection methods for big data presents various applications and advantages. By removing irrelevant and redundant features, feature selection reduces data dimensions, thereby facilitating optimal decision-making within decision systems. One of the key tools for feature selection in hybrid information systems is fuzzy rough set theory. However, this theory faces two significant challenges: First, obtaining fuzzy equivalence relations through intersection operations in high-dimensional spaces can be both time-consuming and memory-intensive. Additionally, this method may produce noisy data, complicating the feature selection *** purpose and innovation of this paper are to address these issues. We proposed a new feature selection model that calculates the combined distance between objects and subsequently used this information to derive the fuzzy equivalence relation. Rather than directly solving the feature selection problem, this approach reformulates it into an optimization problem that can be tackled using appropriate meta-heuristic algorithms. We have named this new approach FSbuHD. The FSbuHD model operates in two modes—normal and optimistic—based on the selection of one of the two introduced fuzzy equivalence relations. The model is then tested on standard datasets from the UCI repository and compared with other algorithms. The results of this research demonstrate that FSbuHD is one of the most efficient and effective methods for feature selection when compared to previous methods and algorithms. ©2025 The author(s).

关键词： Big Data Data Mining Feature Selection Fuzzy Rough Sets Hybrid Information System

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Analysis of linear feedback shift registers and chaos-based techniques for image encryption

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Journal of Cyber Security Technology 2025年第2期9卷 79-87页

作者： Okunbor, Daniel Department of Mathematics and Computer Science Fayetteville State University FayettevilleNC United States

Image encryption is not new. Several different encryption algorithms have been used. Some of the algorithms are based on random sequences generated using chao systems and DNA sequencing and others are based on compression. Still, others are based on Linear Feedback Shift Registers. This paper focuses on research solutions for securing digital images including medical images. Medical images are important and are generally regarded as sensitive in nature. In this research paper, higher order linear feedback shift registers are developed using combination of lower order LFSRs by suitable clocking and the ciphers generated are utilized to encrypt digital images. Our analysis involves using suitable metrics to compare the chaos-based and LFSR-based stream ciphers for image encryption. © 2024 Fayetteville State University. Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cryptography

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A High-Order Hybrid-Spectral Incompressible Navier–Stokes Model for Non-Linear Water Waves

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International Journal for Numerical Methods in Fluids 2025年第6期97卷 1009-1021页

作者： Melander, Anders Ebstrup Bitsch, Max Chen, Dong Peter Engsig-Karup, Allan Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark

We present a new high-order accurate computational fluid dynamics model based on the incompressible Navier–Stokes equations with a free surface for the accurate simulation of non-linear and dispersive water waves in the time domain. The spatial discretization is based on Chebyshev polynomials in the vertical direction and a Fourier basis in the horizontal direction, allowing for the use of the fast Chebyshev and Fourier transforms for the efficient computation of spatial derivatives. The temporal discretization is done through a generalized low-storage explicit fourth-order Runge–Kutta, and for the scheme to conserve mass and achieve high-order accuracy, a velocity-pressure coupling needs to be satisfied at all Runge–Kutta stages. This results in the emergence of a Poisson pressure problem that constitutes a geometric conservation law for mass conservation. The occurring Poisson problem is proposed to be solved efficiently via an accelerated iterative solver based on a geometric (Formula presented.) -multigrid scheme, which takes advantage of the high-order polynomial basis in the spatial discretization and hence distinguishes itself from conventional low-order numerical schemes. We present numerical experiments for validation of the scheme in the context of numerical wave tanks demonstrating that the (Formula presented.) -multigrid accelerated numerical scheme can effectively solve the Poisson problem that constitute the computational bottleneck, that the model can achieve the desired spectral convergence, and is capable of simulating wave-propagation over non-flat bottoms with excellent agreement in comparison to experimental results. © 2025 The Author(s). International Journal for Numerical Methods in Fluids published by John Wiley & Sons Ltd.

关键词： Time domain analysis

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Decay of solutions to a non-degenerate Schrödinger equation with a dynamic boundary of fractional integral type

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SeMA Journal 2025年 1-21页

作者： Elhamid, Hallouz Abd Laboratory of Informatics and Mathematics University of Tiaret Tiaret Algeria Department of Mathematics Faculty of Mathematics and Computer Science Tiaret Algeria

This paper aims to investigate a non-degenerate Schrödinger equation with fractional integral type dynamic boundary control. We focus on establishing the well-posedness of the system by employing semigroup theory. Additionally, we apply the Borichev-Tomilov (Math. Ann. 301 347(2):455–478, 2010) theorem to demonstrate the existence of polynomial decay, which occurs in the absence of exponential stability. © The Author(s), under exclusive licence to Sociedad Española de Matemática Aplicada 2025.

关键词： C0-semigroup Multiplier method Non-exponential stability Well-posedness

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A fusion of CNN And SIFT For multicultural facial expression recognition

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Multimedia Tools and Applications 2025年 1-19页

作者： Qadir, Inam Iqbal, Muhammad Amjad Ashraf, Samman Akram, Sheeraz Department of Computer Science University of Central Punjab Lahore Pakistan Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Lahore Pakistan

The most generic and understandable way of communication is by observing facial expressions;Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm detects the facial region of an image that is most concentrated. Feature Grafting is used to combine high-level and low-level features. A multicultural facial expression database was created using still images of male and female subjects obtained from three datasets: TFEID, Radboud, and JAFFE. The model is designed and tested using this dataset and the proposed technique. The obtained accuracy using the proposed technique was 94.34% which is better than the state-of-the-art method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Grafting (chemical)

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Integrating Blockchain, SSI, and RBAC for the Secure Management of Defence Heritage Buildings

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IEEE Access 2025年 13卷 86290-86307页

作者： Baralla, Gavina Cocco, Luisanna Francesco, Marco Di Tonelli, Roberto University of Cagliari Department of Mathematics and Computer Science Italy

This paper presents a Blockchain-SSI based system designed for the management and monitoring of historical heritage, with a focus on Italian defence heritage buildings. The system supports a modular architecture to integrate cutting-edge technologies, including decentralized applications managed through Smart Contracts and SSI, to enhance security, data integrity, and user autonomy in personal data management. Central to our system is the innovative dual-layer access control that combines SSI with Role-Based Access Control (RBAC), ensuring decentralized and secure management of identities and access permissions. A key component of the building system’s architecture is the blockchain module that enables the certification of data, documents or activities while offering query and verification services on this certified data. Through a detailed exploration of use-case scenarios, this paper illustrates the practical application and benefits of the system, demonstrating its potential to revolutionize heritage conservation practices. The use of Blockchain technology in this context represents an exceptional opportunity for data integrity and reliability among stakeholders, securing and tracing maintenance and processes in historic defence buildings. Additionally, the mechanism for access level control by integrating SSI and RBAC provides robust management of the system while simultaneously empowering individuals to retain control over their data. © 2013 IEEE.

关键词： BIM Blockchain Historical Heritage Management SSI

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A new bi-level deep human action representation structure based on the sequence of sub-actions

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Neural Computing and Applications 2025年第2期37卷 985-1008页

作者： Akhlaghian Tab, Fardin Ramezani, Mohsen Afshoon, Hadi Seyedi, Seyed Amjad Moradyani, Atefeh Department of Computer Engineering Faculty of Engineering University of Kurdistan Sanandaj Iran Department of Computer Science University of Kurdistan Sanandaj Iran

Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of sub-actions. A technique is used to segment actions on the time axis based on body movements via an energy diagram. After dividing actions into sub-actions, a novel bi-level deep structure is used to extract their features. Then, the sequence of sub-action features is modeled by a deep network to create the action model. As extracted sub-actions have fewer variations in execution manner, their representation is more stable, and modeling their sequence would be an efficient model. Experimental results on UCF-YouTube, UCF-Sport, and Human Motion DataBase (HMDB) datasets indicate the sustainable performance of this method. Overall, the accuracy of the proposed method is 0.972 on average, while the value for the second-best method is 0.925. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human form models

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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STFS_TF-IDF _HDSM: semantic and textual feature scaling using TF-IDF based sentiment analysis with Hybrid Deep Sequential Model

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International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Kareem, Fadak R. Obaid, Ahmed J. Faculty of Computer Science and Mathematics University of Kufa Najaf 54001 Iraq

Sentiment analysis in healthcare is critical for the precise identification and assessment of suicidal intentions within clinical notes, enabling timely intervention and improving patient outcomes. The proposed study introduces a novel approach, STFS_TF-IDF_HDSM, where Term Frequency-Inverse Document Frequency (TF-IDF) is employed for Semantic and Textual Feature Scaling with the integration of Hybrid Deep Sequential Model (HDSM) to enhanced sentiment analysis. The variations among features are captured in sentimentsby utilising TF-IDF on the corpus that scales both semantic and textual features effectively. Textual context focuses on surface-level interpretation while semantics focuses on more complex and deeper understanding and reasoning about the text Sequential deep learning produces more precise and refined sentiment predictions in sentiment analysis by effectively capturing the context and emotional flow throughout text sequences. By integrating these Uni-Directional and Bi-Directional techniques with deep sequential processing to refine sentiment analysis for predicting potential suicide intentions achieved accurate results of 98.46% and precision 96.71% with minimum modal loss of 0.3121. This innovative framework demonstrates significant improvements in capturing refined sentiment patterns, offering robust performance across diverse textual datasets. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Attention mechanism Bi-LSTM Feature scaling Gated recurrent unit Sequential deep learning Suicidal sentiments TF-IDF

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New logarithmic step size for stochastic gradient descent

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Frontiers of computer science 2025年第1期19卷 109-118页

作者： Mahsa Soheil SHAMAEE Sajad Fathi HAFSHEJANI Zeinab SAEIDIAN Department of Computer Science Faculty of Mathematical ScienceUniversity of KashanKashan 87317-53153Iran Department of Applied Mathematics Shiraz University of TechnologyShiraz 13876-71557Iran Department of Mathematical Sciences University of KashanKashan 87317-53153Iran

In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an O(1/√T) convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the FashionMinst, CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by 0.9% for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.

关键词： stochastic gradient descent logarithmic step size warm restart technique

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