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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

International Journal of Information Technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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A Numerical Approach For The Algebra of Two-Fold 2 × 2 Fuzzy Real Matrices and Anti Fuzzy Matrices

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Neutrosophic Sets and Systems 2025年 75卷 181-195页

作者： Abubaker, Ahmad A. Abualhomos, Mayada Matarneh, Khaled Al-Husban, Abdallah Faculty of Computer Studies Arab Open University Saudi Arabia Applied Science Private University Amman11931 Jordan Department of Mathematics Faculty of Science and Technology Irbid Jordan National University P.O. Box: 2600 Irbid Jordan Jadara Research Center Jadara University Irbid21110 Jordan

This paper is dedicated to study for the first time the concept of square 2 × 2 fuzzy and anti-fuzzy two-fold matrix with real entries, where we present the two-fold algebraic operations between the these matrices and obtain their special properties such as associativity, commutative properties, and the existing of additive of multiplicative inverses. Also, we provide many examples to explain the two-fold algebraic properties and the two-fold algebraic structure. © 2025, University of New Mexico. All rights reserved.

关键词： Matrix algebra

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EXTREMAL AND CONTINUA SOLUTIONS FOR STOCHASTIC FRACTIONAL DIFFERENTIAL EQUATIONS

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Journal of Mathematical Sciences (United States) 2025年 1-15页

作者： Benhamida, Ouafaa Dahmani, Zoubir Ibrahim, Rabha W. Laboratory of Pure and Applied Maths Faculty of Sciences UMAB Mostaganem Algeria Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia /Mersin 10 99138 Türkiye Department of Computer Science and Mathematics Lebanese American University Beirut 13-5053 Lebanon Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Laboratory of LAMDA-RO Faculty of Sciences University of Blida 1 Blida 09000 Algeria

The aim of this work is to study two classes of stochastic fractional differential equations via the application of the method of upper and lower solutions combined with the Arzela-Ascoli theorem. We begin by proving an auxiliary result for the integral representation of an Airy-type stochastic problem. The specific symmetry features of an Airy-type stochastic problem depend on the form of the stochastic differential equations (SDE) and the relevant coefficients, it is vital to note. To comprehend each issue’s unique symmetries and their effects, a thorough investigation is necessary. Then, we prove an existence result for extremal solutions for the same problem. Another class of stochastic equations of higher-order type is also studied. We also present some examples to show the validity of the obtained results. At the end, a conclusion follows. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Airy equation Extremal solutions Fractional calculus Fractional differential operator Stochastic fractional differential equation Upper and lower method

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Conciliating Privacy and Utility in Data Releases via Individual Differential Privacy and Microaggregation

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Transactions on Data Privacy 2025年第1期18卷 29-50页

作者： Soria-Comas, Jordi Sánchez, David Domingo-Ferrer, Josep Martínez, Sergio Del Vasto-Terrientes, Luis Universitat Oberta de Catalunya Rambla del Poblenou 156 Catalonia Barcelona08018 Spain Department of Computer Engineering and Mathematics CYBERCAT-Center for Cybersecurity Research of Catalonia Universitat Rovira i Virgili Av. Paisos Catalans 26 Catalonia Tarragona43007 Spain

ϵ-Differential privacy (DP) is a well-known privacy model that offers strong privacy guar-antees. However, when applied to data releases, DP significantly deteriorates the analytical utility of the protected outcomes. To keep data utility at reasonable levels, practical applications of DP to data releases have used weak privacy parameters (large ϵ), which dilute the privacy guarantees of DP. In this work, we tackle this issue by using an alternative formulation of the DP privacy guarantees, named ϵ-individual differential privacy (iDP), which causes less data distortion while providing the same protection as DP to subjects. We enforce iDP in data releases by relying on attribute masking plus a pre-processing step based on data microaggregation. The goal of this step is to reduce the sensitivity to record changes, which determines the amount of noise required to enforce iDP (and DP). Specifically, we propose data microaggregation strategies designed for iDP whose sensitivities are significantly lower than those used in DP. As a result, we obtain iDP-protected data with significantly better utility than with DP. We report on experiments that show how our approach can provide strong privacy (small ϵ) while yielding protected data that do not significantly degrade the accuracy of secondary data analysis. © 2025, University of Skovde. All rights reserved.

关键词： Differential privacy

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An Intuitionistic Fuzzy Credibility Model for Exploring Document Ranking Through Similarity Measures in Information Retrieval Systems

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International Journal of Fuzzy Systems 2025年 1-24页

作者： Kausar, Rukhsana Malaika Riaz, Muhammad Simic, Vladimir Pamucar, Dragan Institute of Mathematics University of the Punjab Lahore Pakistan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan School of Engineering Technology Sunway University Selangor Malaysia

In the realm of information retrieval (IR) and multi-criteria decision-making (MCDM), the selection of an appropriate similarity measure is crucial for evaluating keyword significance within large corpora and understanding user behavior. This study explores two foundational methodologies for document ranking: term frequency-inverse document frequency (TF-IDF) and best matching 25 (BM25). These methods provide key insights into the efficiency of information retrieval systems. Additionally, this research introduces an innovative similarity metric grounded in intuitionistic fuzzy credibility (IFC) principles, offering a deeper perspective on document context. Furthermore, the study presents a unique aggregation operator, the intuitionistic fuzzy credibility weighted aggregation (IFCWA) operator, employing the entropy weight method (EWM) to assign weights to decision-makers and criteria. It addresses critical gaps in current assessment methodologies by delving into the pivotal role of these methodologies in assessing keyword significance within textual datasets and their implications for user behavior within the Amazon marketplace. As the digital marketplace continues to evolve, our research aims to bridge critical gaps in keyword significance assessment methodologies, contributing significantly to the development of more sophisticated and effective information retrieval systems that enhance user engagement and decision-making processes. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2025.

关键词： Online searching

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Hybrid Profile based Multi-document Text Summarisation 4

Hybrid Profile based Multi-document Text Summarisation

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Fendji, Jean Louis K.E. Donatien, Dounia Atemkeng, Marcellin Department of Computer Engineering University Institute of Technology University of Ngaoundere Ngaoundere455 Cameroon Wallenberg Research Centre Stellenbosch University Stellenbosch South Africa Department Mathematics and Computer Science Faculty of Science University of Ngaoundere Ngaoundere454 Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

The internet has become a crucial component of the daily routines, offering numerous resources and documents for a variety of tasks and information retrieval. However, the large volume of available information often leads to "information saturation,"posing a challenge to efficient processing and extraction of relevant information. To mitigate this issue, extensive research has been conducted exploring a range of methods, including machine learning and deep learning techniques. A significant advancement in this field is automatic text summarisation, which employs Natural Language Processing (NLP). Despite their efficacy, traditional summarisation methods typically fall short as they fail to consider the unique needs and preferences of individual users. This study introduces a novel, hybrid and profile-based multi-document summarisation method that selects relevant documents according to user queries and preferences, as defined in a user profile. By leveraging NLP algorithms, the proposed system creates personalised summaries by initially extracting sentences from documents that closely match the user's profile, followed by the generation of a concise abstract summary. The model, specifically developed for French, results in a success rate of 87.5%, and delivering semantically coherent summaries for up to three documents concurrently. This method enhances the user experience by providing succinct and customised information. © 2025 The Authors. Published by Elsevier B.V.

关键词： Multi-document preferences Text Summarisation User Profile

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Friction Dissymmetry on Hexagonal Boron Carbon Nitride

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Nano Letters 2025年第19期25卷 7909-7915页

作者： Lu, Leiling Ying, Tianquan Cui, Chuan-Xin Chen, Yang Leng, Jiantao Li, Jianxin Jiang, Jin-Wu Chang, Tienchong Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Frontier Science Center of Mechanoinformatics School of Mechanics and Engineering Science Shanghai University Shanghai200072 China Department of Engineering Mechanics School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Joint-Research Center for Computational Materials Zhejiang Laboratory Hangzhou311100 China

The classical friction law posits that macroscopic friction is directly proportional to the normal force, represented by a constant friction coefficient. However, frictional behavior becomes increasingly complex at the nanoscale. We reveal a counterintuitive instance of dissymmetric friction during the sliding of a graphene flake on a hexagonal boron carbon nitride (h-BCN) substrate. This dissymmetry is marked by significantly different friction coefficients in opposing directions along the same sliding path. We attribute this unexpected behavior to the dissymmetric potential energy landscape of h-BCN, which fundamentally differs from the symmetrical profiles seen in graphene and h-BN, despite h-BCN being perceived as a composite of these materials. Our findings enhance the understanding of nanoscale friction and pave the way for the development of innovative nanodevices that utilize directional friction control. © 2025 American Chemical Society.

关键词： Nanocomposites

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Machine Learning Approaches in Cybersecurity to Enhance Security in Future Network Technologies

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SN computer Science 2025年第4期6卷 1-18页

作者： Ahmad, Sultan Haque, Md. Alimul Abdeljaber, Hikmat A. M. Eljialy, A.E.M. Nazeer, Jabeen Mishra, B.K. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Mohali Punjab Gharuan 140413 India Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Information Systems College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Department of Physics Veer Kunwar Singh University Ara 802301 India

As technology continues to evolve rapidly, cybersecurity has become a critical global concern. The increasing sophistication of cyber threats poses significant risks to individuals, businesses, and governments. To combat these threats, cybersecurity tools play a crucial role in monitoring, detecting, and mitigating security risks in digital environments. These tools ensure data protection, prevent unauthorized access, and safeguard sensitive information. However, traditional cybersecurity approaches are struggling to keep pace with emerging cyber-attacks. In response, machine learning (ML) has emerged as a powerful solution for enhancing cybersecurity strategies. ML algorithms enable organizations to analyze large datasets, identify anomalies, and predict potential threats with greater accuracy. Cybersecurity tools, integrated with ML, act as the final line of defense against attacks such as data breaches, identity theft, and system intrusions. This research explores the application of ML in selecting and optimizing cybersecurity models for enterprise ICT systems. It also emphasizes the growing demand for skilled professionals who can develop and implement ML-based security solutions. By examining current trends and future possibilities, this study provides valuable insights into the role of ML in strengthening cybersecurity measures and enhancing overall digital protection for organizations worldwide. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber security Cybercrime Data analysis ML algorithms Network security Threats

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand 10

Negative Binomial Regression Model for Predicting the Number...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Thepsumritporn, Sthaporn Jitpattanakul, Anuchit Mekruksavanich, Sakorn Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

This article examines the use of count data models to predict the number of tourists visiting Thailand's national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models - Poisson (POI), and Negative Binomial (NB) - were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data's overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model's utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data. © 2025 IEEE.

关键词： Logistic regression

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (Science and Information Organization). All rights reserved.

关键词： Students

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