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Advancing Web Browser Forensics: Critical Evaluation of Emerging Tools and Techniques

SN Computer Science 2025年第4期6卷 1-28页

作者： Chand, Rishal Ravikesh Sharma, Neeraj Anand Kabir, Muhammad Ashad Department of Computer Science and Mathematics The University of Fiji Lautoka Fiji School of Computing Mathematics and Engineering Charles Sturt University Bathurst 2795 NSW Australia

As the use of web browsers continues to grow, the potential for cybercrime and web-related criminal activities also increases. Digital forensic investigators must understand how different browsers function and the critical areas to consider during web forensic analysis. Web forensics, a subfield of digital forensics, involves collecting and analyzing browser artifacts, such as browser history, search keywords, and downloads, which serve as potential evidence. While existing research has provided valuable insights, many studies focus on individual browsing modes or limited forensic scenarios, leaving gaps in understanding the full scope of data retention and recovery across different modes and browsers. This paper addresses these gaps by defining four browsing scenarios and critically analyzing browser artifacts across normal, private, and portable modes using various forensic tools. We define four browsing scenarios to perform a comprehensive evaluation of popular browsers—Google Chrome, Mozilla Firefox, Brave, Tor, and Microsoft Edge—by monitoring changes in key data storage areas such as cache files, cookies, browsing history, and local storage across different browsing modes. Overall, this paper contributes to a deeper understanding of browser forensic analysis and identifies key areas for enhancing privacy protection and forensic methodologies. © The Author(s) 2025.

关键词： Browser artifacts Browser forensics Cybercrime Digital forensics Forensics tool Web browser

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Modeling uncertainties associated with decision-making algorithms based on similarity measures of possibility belief interval-valued fuzzy hypersoft setting

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Neutrosophic Sets and Systems 2025年 77卷 282-309页

作者： Romdhini, Mamika Ujianita Al-Sharqi, Faisal Al-Obaidi, R.H. Rodzi, Zahari Md. Department of Mathematics Faculty of Mathematics and Natural Science University of Mataram Mataram83125 Indonesia Department of Mathematics Faculty of Education for Pure Sciences University of Anbar Ramadi55431 Iraq Fuel and Energy Techniques Engineering Department College of Engineering and Technologies Al-mustaqbal University Babylon51001 Iraq College of Computing Informatics and Mathematics UiTM Cawangan Negeri Sembilan Kampus Seremban Negeri Sembilan 72000 Malaysia

Hypersoft sets (HSSs) were initiated as an extension of soft sets (SSs) to address real-life scenarios involving multiple disjoint sets with different traits. One such extension is the interval-valued fuzzy hypersoft set (IVFHSS), which has proven effective in decision-making (DM). However, the IVFHSS model lacks a mechanism to incorporate the degree of acceptance of DM opinions, which is crucial for accurate decision-making. To overcome this limitation, our work aims to develop a novel hyperstructure called a possibility interval-valued fuzzy hypersoft set (PIVFHS-set). We begin by introducing essential operations and their properties, such as PIVFHS-subset, PIVFHS-null set, PIVFHS-absolute set, and complement of a PIVFHS-set. These concepts are illustrated through numerical examples to demonstrate their practical applications. Next, we delve into set-theoretic operations of PIVFHS sets, including union, intersection, AND, OR, and relevant laws. These operations are further elucidated through numerical examples, matrix representations, and graphical illustrations. Additionally, we present two algorithms based on AND and OR operations, providing step-by-step explanations and showcasing their effectiveness through illustrative examples. Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. Alongside an analytical study of the advantages and disadvantages of this model, we provide suggestions for future research based on the identified limitations. © 2025, University of New Mexico. All rights reserved.

关键词： Fuzzy sets

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GAN-Guarded Fine-Tuning: Enhancing Adversarial Robustness in NLP Models 5

GAN-Guarded Fine-Tuning: Enhancing Adversarial Robustness in...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Ali, Tarek Eleyan, Amna Al-Khalidi, Mohammed Bejaoui, Tarek Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798331519957

The increasing occurrence of assaults on Natural Language Processing (NLP) systems has sparked worry about their resilience and trustworthiness, particularly in crucial applications like sentiment analysis, machine translation and natural language deduction. To tackle these weaknesses this research presents GAN-Guarded Fine-Tuning (GAN-FT) an training structure that utilizes Generative Adversarial Networks (GANs) to bolster the resilience of extensive language models such, as BERT and RoBERTa. In contrast, training GAN with feature matching (GAN FW) uses a generative opponent to generate various and logically connected adversarial instances with less computational burden. Regular assessments on datasets such as IMDB, Yelp and SNLI indicate that GAN-FT consistently decreases the effectiveness of attacks while enhancing accuracy and adaptability across domains. This suggests its effectiveness in countering familiar and new word substitution forms of attack. This study highlights the impact of incorporating GANs into training methods and sheds light on understanding models better and making decisions effectively in this field of research. Though there are scalability and quick implementation challenges, in real-Time scenarios, GAN-FT is a starting point for looking into adversarial defence mechanisms and broadening its use in more intricate NLP assignments. The research advances the reliability and security aspects of NLP systems, which is a move towards combatting adversarial risks in the ever-changing AI domain. © 2025 IEEE.

关键词： Generative adversarial networks

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Leveraging A Deep Learning-Based Privacy Compliance Framework for IoT Applications 5

Leveraging A Deep Learning-Based Privacy Compliance Framewor...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Eleyan, Amna Ahmed, Hamza Bejaoui, Tarek Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom University of Carthage Computer Engineering Department Tunisia

ISBN: (纸本)9798331519957

This paper explores and investigates the development and implementation of a deep learning-based privacy compliance framework for Internet of Things (IoT) applications. The geometric growth in potentially sensitive data from the vast generations of data as IoT devices proliferate raises critical security and privacy concerns for individuals and organisations. Additionally, rising cybersecurity attacks reshape how data is handled, especially with IoT devices storing sensitive client data. Simultaneously, GDPR and CCPA which are rigorous data protection regulations have created an urgent and imperative need for automated compliance solutions. Utilising these regulatory standards, this research harnesses deep learning methodologies, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to tackle these complex challenges while employing public IoT datasets to test. Cutting-edge features are incorporated into the proposed deep-learning framework including threat characterising, anomaly detection, dynamic policy adjustment, and regulatory compliance checking/verification for IoT environments, with the additionality of a devised pipeline encompassing comprehensive data processing, model training, threat detection, compliance checking, and an intuitive user-friendly alert system. The findings concluded that the deep-learning models achieved high accuracy of 99% in threat detection, with general compliance achieving 91% and over of its IoT dataset (RT-IoT2022), concurrently while the integrated compliance checking functions effectively enforced privacy policies from a 'docx' file. This research contributes significantly to the relevant field by offering a novel, automated approach to handling privacy compliance in intricate IoT landscapes, prospectively reducing organisational resource expenditure while enhancing overall data protection measures. © 2025 IEEE.

关键词： Differential privacy

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Astronomical Image Clustering Using Spectral, K-Means and Mean Shift

Astronomical Image Clustering Using Spectral, K-Means and Me...

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2025 International Conference on computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Mohite, Vaishnavi Shivaji Patra, Krishna Gayatri Sankar, C.H. Hari Jayan, Sarada Amrita School of Computing Department of Computer Science Bengaluru India Amrita School of Engineering Department of Mathematics Bengaluru India

ISBN: (纸本)9798331538538

By analyzing vast amounts of observational data through astronomical image analysis, you can gain valuable information about the structure of the universe. This paper presents a new method for clustering astronomical images that uses both spectral and K-means methods and also incorporates linear algebra and optimization principles. We propose a method that combines feature extraction techniques such as the Fourier transform with clustering algorithms to classify celestial objects based on their characteristics. The research examines the effectiveness of spectral clustering and K-means clustering in organizing astronomical images into meaningful groups, taking into account factors such as computer efficiency, clustering accuracy and interpretability. Experimenting with different datasets, including telescope images, we demonstrate the effectiveness of our approach in automating the classification of astronomical objects. Our results reveal insights into the underlying structure of astronomical knowledge and offer promising avenues for future research in computational astronomy. In general, this research advances both the field of astronomy and the application of mathematical methods to image analysis and clustering. © 2025 IEEE.

关键词： K-means clustering

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AG Codes Have No List-Decoding Friends: Approaching the Generalized Singleton Bound Requires Exponential Alphabets

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IEEE Transactions on Information Theory 2025年第4期71卷 2443-2451页

作者： Alrabiah, Omar Guruswami, Venkatesan Li, Ray University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94709 United States University of California at Berkeley Department of Electrical Engineering and Computer Science Department of Mathematics Simons Institute for the Theory of Computing BerkeleyCA94709 United States Santa Clara University Department of Mathematics and Computer Science Santa ClaraCA95053 United States

A simple, recently observed generalization of the classical Singleton bound to list-decoding asserts that rate R codes are not list-decodable using list-size L beyond an error fraction L/L+1 (1-R) (the Singleton bound being the case of L=1 , i.e., unique decoding). We prove that in order to approach this bound for any fixed L > 1 , one needs exponential alphabets. Specifically, for every L > 1 and R ∈ (0,1) , if a rate R code can be list-of-L decoded up to error fraction L/L+1 (1-R - ∈ ) , then its alphabet must have size at least exp (ΩL,R(1\ ∈)) . This is in sharp contrast to the situation for unique decoding where certain families of rate R algebraic-geometry (AG) codes over an alphabet of size O(1\ ∈2) are unique-decodable up to error fraction (1-R-\ ∈ )2 . Our bounds hold even for subconstant ∈ ≥ 1/n , implying that any code exactly achieving the L-th generalized Singleton bound requires alphabet size 2ΩL,R(n) . Previously this was only known only for L=2 under the additional assumptions that the code is both linear and MDS. Our lower bound is tight up to constant factors in the exponent—with high probability random codes (or, as shown recently, even random linear codes) over exp(OL(1\ ∈)) -sized alphabets, can be list-of-L decoded up to error fraction L/L+1 (1-R -∈ ) . © 1963-2012 IEEE.

关键词： Errors

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A deep learning approach for recognizing and solving handwritten mathematical equations

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Neural computing and Applications 2025年第14期37卷 8759-8772页

作者： Yadav, Pavinder Shantilal, Sankhala Bhavik Kumar, Vipin Sihag, Parveen Sharma, Pawan Kumar Rana, Pankaj Department of Mathematics and Scientific Computing National Institute of Technology HP Hamirpur India Department of Civil Engineering Chandigarh University Punjab Mohali India Department of Physical Sciences Baba Mastnath University Asthal Bohar Haryana Rohtak India

Recognizing handwritten equations is considerably more challenging than identifying printed ones, primarily due to variations in writing styles. Even though there have been recent improvements in handwriting recognition for things like equation solvers and automation, it is still hard for machines to understand a handwritten mathematical expression (HME). This paper presents a deep learning model for recognizing and solving handwritten equations, as well as its graphical user interface (GUI). A manually constructed large dataset comprising approximately 66k images of digits, mathematical symbols, and characters is utilized, with a fine-tuned InceptionV3 model employed for image classification and segmentation. The system is designed to extract handwritten equations from an image, perform character recognition, and generate the roots of the polynomial equation up to degree four. According to the findings, the trained model achieves remarkable accuracy in recognizing and solving handwritten equations, with over 94% accuracy on the training set. By eliminating the possibility of human error, automatic handwritten equation solvers provide more accurate results than manual methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Enhancing Gallium Arsenide Semiconductor Chip Quality Classification through AI Algorithms

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Sensors and Materials 2025年第5期37卷 1785-1807页

作者： Luo, Guofeng Lee, Hsiao-Yi Wang, Kenvi Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Department of Computing and Mathematics Liverpool John Moores University United Kingdom

Deep learning algorithms, such as those employed by CHATGPT/artificial neural networks, have served as transformative catalysts across various sectors, significantly impacting our daily lives. Within the realm of semiconductor processes, the metric of yield stands as a crucial performance benchmark directly influencing the sustained success of enterprises. Among batches of chips, inherent disparities in quality persist, with each chip grade necessitating an appropriate operational environment. To preempt the pitfalls of customer returns or factory repairs, thereby mitigating manufacturing costs, in this study, we embark on a comprehensive exploration utilizing AI algorithms for quality classification. In pursuit of bolstering the competitive edge of semiconductor firms, the refinement of gallium arsenide chip quality classification to augment yield emerges as a paramount concern. Moreover, in this research endeavor, we seek to discern the physical parameters underlying GaAs defect products, offering pivotal insights to enhance the manufacturing process—an aspect poised to be a pivotal focal point. © MYU K.K.

关键词： accuracy AI artificial neural network gallium arsenide machine learning

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Barber Optimization Algorithm:A New Human-Based Approach for Solving Optimization Problems

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Computers, Materials & Continua 2025年第5期83卷 2677-2718页

作者： Tareq Hamadneh Belal Batiha Omar Alsayyed Widi Aribowo Zeinab Montazeri Mohammad Dehghani Frank Werner Haider Ali Riyadh Kareem Jawad Ibraheem Kasim Ibraheem Kei Eguchi Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Department of Mathematics Faculty of Science and Information TechnologyJadara UniversityIrbid21110Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Electrical Engineering Faculty of Vocational StudiesUniversitas Negeri SurabayaSurabaya60231Indonesia Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Cybersecurity and Cloud computing Technical EngineeringUruk UniversityBaghdad10001Iraq Department of Medical Instrumentations Techniques Engineering Al-Rasheed University CollegeBaghdad10001Iraq Department of Electrical Engineering College of EngineeringUniversity of BaghdadBaghdad10001Iraq Department of Information Electronics Fukuoka Institute of TechnologyFukuoka8110295Japan

In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the *** processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for *** performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test *** comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark *** validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable *** results confirm BaOA’s versatility and reliability as an optimization *** study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.

关键词： Optimization metaheuristic barber hairstyle human-based algorithm exploration exploitation

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Connectivity number in fuzzy graphs with application in mobile communication networks

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Journal of Applied mathematics and computing 2025年 1-27页

作者： Rezayi, M. Eslahchi, Ch. Borzooei, R.A. Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Data and Computer Sciences Shahid Beheshti University Tehran Iran Soft Computing Center Department of Mathematics Faculty of Mathematical Sciences Shahid Beheshti University Tehran Iran Department of Mathematics Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey

This paper explores the concept of connectivity within fuzzy graphs, extending classical graph theory to model uncertainty in real-world networks. We introduce the Connectivity Number (CN) as a novel and computationally efficient metric to assess network robustness. CN quantifies the minimum strength of connectedness for any vertex in a fuzzy graph, identifying the weakest links that influence overall stability. Theoretical properties of CN are established in various fuzzy graph structures, including strong fuzzy graphs, fuzzy cycles, and complete fuzzy graphs. We further investigate the behavior of CN under six fundamental fuzzy graph operations: Cartesian product, normal product, tensor product, composition, lexicographic max product, and lexicographic min product, revealing critical insights into connectivity in composite structures. Furthermore, we demonstrate the practical relevance of CN by applying it to a mobile communication network, where it effectively identifies weak connections and informs optimization strategies. Compared to existing indices such as the Wiener index (WI) and the connectivity index (CI), CN provides a simpler and more direct measure of connectivity weaknesses, making it a valuable tool for network optimization, resilience analysis, and decision-making. Future research will explore CN in dynamic fuzzy graphs, examine its stability under network perturbations, and develop efficient algorithms for large-scale applications. © The Author(s) under exclusive licence to Korean Society for Informatics and Computational Applied mathematics 2025.

关键词： Network theory (graphs)

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