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Systematic Literature Review for Optimization System with Advection-Diffusion-Reaction Non-Linear Equation in Water Quality

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IAENG International Journal of Applied mathematics 2024年第12期54卷 2541-2554页

作者： Suryana, Ino Chaerani, Diah Abdullah, Atje Setiawan Prabuwono, Anton Satria Muslihin, Khoirunnisa Rohadatul Aisy Irmansyah, Athaya Zahrani Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

Water quality is an essential component of environmental health and sustainability, including various parameters such as chemical, physical, and biological attributes. These parameters determine the suitability of water for consumption, recreation, and ecosystem health. Clean and safe water availability is a crucial foundation for human well-being, ecological balance, and sustainable development, indicating the need to monitor and control water quality to meet human demands. Based on the importance of water quality, this research aimed to provide a comprehensive overview of previous investigations related to the optimization system of the advection-diffusionreaction (ADR) non-linear equation in water quality. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were conducted to ensure a structured article selection, data extraction, and analysis approach. This was followed by presenting a bibliometric analysis of selected data to support the study of research trends. The results showed that using ADR non-linear equation systems in modeling water quality optimization was a potential area requiring further exploration. Moreover, this research provided valuable information that could be used as a reference for further investigation of water quality optimization. A future research design is also presented. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Advection-diffusion-reaction Optimization PRISMA Sustainable Development Goals (SDGs) Systematic literature review Water quality

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Traffic Detection and Forecasting from Social Media Data Using a Deep Learning-Based Model, Linguistic Knowledge, Large Language Models, and Knowledge Graphs 16

Traffic Detection and Forecasting from Social Media Data Usi...

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16th International Conference on Knowledge Engineering and Ontology Development, KEOD 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Melhem, Wasen Abdi, Asad Meziane, Farid Department of Computing and Mathematics Faculty of Science and Engineering University of Derby United Kingdom

ISBN: (纸本)9789897587160

Traffic data analysis and forecasting is a multidimensional challenge that extracts details from sources such as social media and vehicle sensor data. This study proposes a three-stage framework using Deep Learning (DL) and natural language processing (NLP) techniques to enhance the end-to-end pipeline for traffic event identification and forecasting. The framework first identifies relevant traffic data from social media using NLP, context, and word-level embeddings. The second phase extracts events and locations to dynamically construct a knowledge graph using deep learning and slot filling. A domain-specific large language model (LLM), enriched with this graph, improves traffic information relevancy. The final phase integrates Allen's interval algebra and region connection calculus to forecast traffic events based on temporal and spatial logic. This framework’s goal is to improve the accuracy and semantic quality of traffic event detection, bridging the gap between academic research and real-world systems, and enabling advancements in intelligent transport systems (ITS). © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Knowledge graph

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Hybrid intelligent system for channel allocation and packet transmission in CR-IoT networks

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International Journal of Hybrid Intelligent Systems 2024年第2期20卷 101-117页

作者： Asuquo, Daniel E. Umoh, Uduak A. Robinson, Samuel A. Dan, Emmanuel A. Udoh, Samuel S. Attai, Kingsley F. Department of Information Systems Faculty of Computing University of Uyo TETFund Center of Excellence in Computational Intelligence Research University of Uyo Department of Cyber Security Faculty of Computing University of Uyo Department of Computer Science Faculty of Computing University of Uyo Department of Data Science Faculty of Computing University of Uyo Department of Mathematics and Computer Science Ritman University Ikot Ekpene

The proliferation of interconnected devices is driving a surge in the demand for wireless spectrum. Meeting the need for wireless channel access for every device, while also ensuring consistent quality of service (QoS), poses significant challenges. This is particularly true for resource-limited heterogeneous devices within Internet of Things (IoT) networks. Cognitive radio (CR) technology addresses the shortcomings of traditional fixed channel allocation policies by enabling unlicensed users to opportunistically access unused spectrum belonging to licensed users. This facilitates timely and reliable transmission of mission-critical data packets. A cognitive radio-enabled IoT (CR-IoT) network is poised to better accommodate the growing demands of diverse applications and services within the smart city framework, spanning areas such as healthcare, agriculture, manufacturing, logistics, transportation, environment, public safety, and pharmaceuticals. To minimize switching delays and ensure energy and spectral efficiency, this study proposes a hybrid intelligent system for efficient channel allocation and packet transmission in CR-IoT networks. Leveraging Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS), the system dynamically manages spectrum resources to minimize handoffs while upholding QoS. A Java-based simulation integrates system outputs with interference temperature data to accommodate service demands across 2G-4G spectrums. Evaluation reveals SVM's 98.8% accuracy in detecting spectrum holes and ANFIS's 90.4% accuracy in channel allocation. These results demonstrate significant potential for enhancing spectrum utilization in various IoT applications. © 2024-IOS Press. All rights reserved.

关键词： Cognitive radio network intelligent whitespace detection internet of things optimized channel allocation packet transmission

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On some properties of the bibasic Humbert hypergeometric functions Ξ_(1) and Ξ_(2)

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Applied mathematics(A Journal of Chinese Universities) 2023年第4期38卷 614-630页

作者： CAI Qing-bo Ghazi S.Khammash Shimaa I.Moustafa Ayman Shehata Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou 362000China Department of Mathematics Al-Aqsa UniversityGazaGaza StripPalestine Department of Mathematics Faculty of ScienceAssiut UniversityAssiut 71516Egypt Department of Mathematics Unaizah College of Science and ArtsUnaizah 56264Qassim UniversityBuraydah 52571QassimSaudi Arabia

The main object of this paper is to deduce the bibasic Humbert functions Ξ_(1) and Ξ_(2)Some interesting results and elementary summations technique that was successfully employed,q-recursion,q-derivatives relations,the q-differential recursion relations,the q-integral representations for Ξ_(1) and Ξ_(2)are *** summation formula derives a list of p-analogues of transformation formulas for bibasic Humbert functions that have been studied,also some hypergeometric functions properties of some new interesting special cases have been given.

关键词： q-calculus bibasic Humbert hypergeometric functions q-derivative

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Data-Driven Framework for Optimizing Internship Efficiency and Addressing Skills Mismatch in Malaysian Higher Education 22

Data-Driven Framework for Optimizing Internship Efficiency a...

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22nd International Learning and Technology Conference, L and T 2025

作者： Abas, Ashardi Ibrahim, Mohd Mokhzani Bin Azmi, N.H. University Pendidikan Sultan Idris Faculty of Computing Meta Technology Perak Malaysia University Pendidikan Sultan Idris Faculty of Science and Mathematics Perak Malaysia

ISBN: (纸本)9798331532215

The persistent skills mismatch between higher education outcomes and labour market needs presents a significant challenge for graduate employability, particularly in fast-evolving sectors such as artificial intelligence (AI) and data science. This paper introduces a data-driven internship framework designed to bridge this gap by optimising student placements, providing real-time feedback, and ensuring academic curricula remain aligned with labour market demands. The framework integrates three systems: the Student Industry Placement System (SIPS), which uses machine learning to match students with relevant internships;the Intern Learning Management System (ILMS), which tracks skill development through continuous feedback loops;and the Industry Profiling System (IPS), which leverages predictive analytics to provide real-time labour market insights. The framework was evaluated through a mixed-methods approach, incorporating pilot program data from five universities and 15 industry partners and simulated data based on historical internship trends. The SIPS algorithm achieved a placement accuracy of 89%, significantly improving over traditional methods (60-65%). 78% of students received continuous feedback through the ILMS, resulting in measurable improvements in technical skills (30% increase in coding proficiency) and problem-solving abilities (25% improvement). The IPS enabled universities to update 15% of their curricula in response to real-time labour market data, particularly in high-growth sectors such as AI and digital transformation. 68% of the students secured full-time employment within six months of graduation, with 65% in roles directly aligned with their internships. These findings demonstrate the framework's scalability and potential for reducing skills mismatch, making it a viable model for other emerging economies. © 2025 IEEE.

关键词： Commerce

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METRICALLY REGULAR MAPPING AND ITS UTILIZATION TO CONVERGENCE ANALYSIS OF A RESTRICTED INEXACT NEWTON-TYPE METHOD

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Journal of Computational mathematics 2022年第1期40卷 44-69页

作者： Mohammed Harunor Rashid Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Department of Mathematics Faculty of ScienceUniversity of RajshahiRajshahi-6205Bangladesh

In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set-valued mapping with closed *** establish the convergence criteria of the restricted inexact Newton-type method,which guarantees the existence of any sequence generated by this method and show this generated sequence is convergent linearly and quadratically according to the particular assumptions on the Frechet derivative of ***,we obtain semilocal and local convergence results of restricted inexact Newton-type method for solving the above generalized equation when the Frechet derivative of f is continuous and Lipschitz continuous as well as f+F is metrically *** application of this method to variational inequality is *** addition,a numerical experiment is given which illustrates the theoretical result.

关键词： Generalized equation Restricted inexact Newton-type method Metrically regular mapping Partial Lipschitz-like mapping Semilocal convergence.

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Analysis and Prediction of Purchase Intention of Online Customers with Deep Learning 1

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Bansal, Megha Vyas, Vaibhav Department of Computer Science Faculty of Mathematics and Computing Banasthali Vidyapith Rajasthan Aliyabad India

ISBN: (数字)9789811976155

ISBN: (纸本)9789811976148

Nowadays, online shopping has transformed the traditional shopping trend enormously. This shift has taken a altogether enlarged view after the COVID-19 pandemic which provided world full of opportunities for customers to make purchasing online with ease of home and without compromising the safety parameters. But now every new or old, small, or big platform wants to grab their customers at any cost for which they really need to understand the demand or expectations of customer popping in. Understanding the need of the customer is the key of success for online shopping sites. In our paper, we have made use of a deep learning model to first predict whether the customer is going to make a purchase or not. After deciding this broader category, we can actually provide offers for non-purchaser or a better deal which can make them shop, and for purchasers, we can provide some loyalty cash or coupons to make them stay. In our model, we have got training accuracy of 90.24% and validation accuracy of 88.15%. And the loss of both is 23% and 28%, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sales

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish 30

Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets fo...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lango, Marta Naglik, Borys Lango, Mateusz Naglik, Iwo Poznan University of Technology Faculty of Computing and Telecommunications Poland Adam Mickiewicz University Faculty of Mathematics and Computer Science Poland Charles University Faculty of Mathematics and Physics Institute of Formal and Applied Linguistics Czech Republic

ISBN: (纸本)9782493814104

Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. It concerns the construction of triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarity. Despite the growing popularity of the task and the many machine learning methods being proposed to address it, the number of datasets for ASTE is very limited. In particular, no dataset is available for any of the Slavic languages. In this paper, we present two new datasets for ASTE containing customer opinions about hotels and purchased products expressed in Polish. We also perform experiments with two ASTE techniques combined with two large language models for Polish to investigate their performance and the difficulty of the assembled datasets. The new datasets are available under a permissive licence and have the same file format as the English datasets, facilitating their use in future research. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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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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