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Intelligent QV2X Routing for Traffic Management in Consumer IoV Using STGNN and Reinforcement Learning

IEEE Transactions on Consumer Electronics 2025年

作者： Jamal, Syed Saqib Muhammad, Afaq Song, Wang-Cheol Jeju National University Department of Electronic Engineering Jeju Korea Republic of College of Computing and Mathematics Computer Engineering Department and Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

In the Consumer Internet of Vehicles (CIoV), reliable and timely data communication is essential for enhancing driver experience and safety. This paper introduces an innovative QV2X routing strategy that uses Spatio-Temporal Graph Neural Networks (STGNN) and Q-learning to optimize packet traffic in CIoV. By predicting network conditions and adapting to real-time data flows, our approach directly addresses consumer needs for efficient data transmission, reduced communication delays, and improved infotainment access. The integration of predictive models with adaptive learning mechanisms not only optimizes packet delivery but also minimizes latency and packet loss, critical for consumer applications like real-time navigation assistance and hazard warnings. Our key contribution is a dynamic packet traffic management system designed for consumer use, enhancing network reliability and efficiency for everyday vehicle users. Experimental results validate that our model surpasses existing benchmarks by improving packet delivery ratios by up to 15% and reducing end-to-end delays by up to 20% in urban traffic scenarios. This advancement demonstrates our strategy's effectiveness in enriching consumer experiences and safety in vehicular communications. © 1975-2011 IEEE.

关键词： Packet loss

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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

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Fractional Modeling for Improving Scholastic Performance of Students with Optimal Control

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International Journal of applied and Computational mathematics 2022年第1期8卷 1-20页

作者： Yusuf, Abdullahi Qureshi, Sania Mustapha, Umar T. Musa, Salihu S. Sulaiman, Tukur A. Department of Computer Engineering Biruni University Istanbul Turkey Department of Mathematics Science Faculty Federal University Dutse Jigawa Dutse 7156 Nigeria Department of Basic Sciences and Related Studies Mehran University of Engineering and Technology Jamshoro 76062 Pakistan Department of Mathematics Near East University TRNC Mersin 10 Turkey Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong Department of Mathematics Kano University of Science and Technology Wudil Nigeria

Students’ day-to-day activities can be influenced by internal and external factors that can cause academic turbulence. These factors vehemently impart to the setback of bad scholastic performance in an academic arena that is escalating among pupils of one of the Nigerian universities namely, KUST. The current paper is an extension and analysis of such a problem categorized as a contagious disease. The extension of the model is from a fractional viewpoint characterizing how this difficulty is escalated in the KUST campus. The new extensions of the model are in the sense of Caputo, Caputo–Fabrizio–Caputo and the Atangana–Baleanu–Caputo and the dimensionality for the parameters has been utilized. To this aim, the existence and uniqueness of the solutions of the model under consideration are proved by using the fixed point theory. Furthermore, we have also analyzed the positivity and boundedness of the solution for the proposed scholastic model with the Caputo differential operator. The fractional scholastic models unveil the persistence of the turbulence in the campus that can be predicted through the reproduction numbers. We also applied optimal control strategies to the proposed fractional model. The analysis shows that the positive change of behavior towards academic failure (u₁) and proper orientation (u₂) are effective measures in minimizing the problem in the compartments of below-average and weak students. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Endemic Fixed point analysis Fractional operators Invariant region Non-Markovian Optimal control

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Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning 15

Identifying Cell Types in Single-Cell Multimodal Omics Data ...

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15th International Conference on Knowledge and Systems engineering, KSE 2023

作者： Do, Van Hoan Canzar, Stefan Le Quy Don Technical University Center for Applied Mathematics and Informatics Hanoi Viet Nam Huck Institutes of the Life Sciences The Pennsylvania State University University Department of Computer Science and Engineering ParkPA16802 United States

ISBN: (纸本)9798350329742

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods. © 2023 IEEE.

关键词： Cells

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Efficient Hybrid Technique for Devanagari Numerals Recognition

Efficient Hybrid Technique for Devanagari Numerals Recogniti...

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Bura, Deepa Singh, Meeta Nandal, Poonam Khurana, Pooja Faculty of Engineering & Technology Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering Faridabad India Manav Rachna International Institute of Research and Studies Department of Applied Sciences Faculty of Engineering & Technology Faridabad India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

As a subset of Pattern Recognition, Handwritten Devanagari Character Recognition (HDCR) is one of the exciting field of research from decades but Devanagari numerals don't receive proper attention from the researchers. In India, Government offices provide preferences for filling the application form in Hindi or English & use the scanner for making the record of those applications. But the drawback of the scanned form is that they cannot be edited without using HDCR. This paper proposes a Handwritten Devanagari Numerals Recognition (HDNR) technique which is used to identify Devanagari numerals from given digital images, without human intervention. In this paper a novel technique is proposed which perform pre-processing on the inputted image where features are extracted & based on those features Devanagari numerals are classified. © 2022 IEEE.

关键词： Character recognition

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Comparison of multi-criteria decision methods for customer-centered decision making: A practical study case 27

Comparison of multi-criteria decision methods for customer-c...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Shekhovtsov, Andrii Dobryakova, Larisa Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

In this paper, we show the practical application of two recently proposed Multi-Criteria Decision Analysis (MCDA) methods, namely the Stable Preference Ordering Towards the Ideal Solution (SPOTIS) and the Reference Ideal Method (RIM) methods. Both of these methods can utilize the Expected Solution Point (ESP) concept, which allows it to easily reflect the decision-maker's or the customer's preferences and expectations. We show the comparison of those methods in the practical study case of laptop choice for the customer with specific needs and expectations for the hardware expressed using ESP. The comparison also includes rankings built toward an optimal solution to underline the necessity of such an approach as the Expected Solution Point. We also use the recently developed Ranking Comparison (RANCOM) method to identify criteria weights to include customer preferences in the weights. The paper also contains a broad discussion of the obtained results and limitations of the presented methods. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： ESP MCDA RANCOM RIM SPOTIS Target-based decision-making

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Personalized microblog recommendations based on trustpropagation and implicit microblog similarity

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Frontiers of computer Science 2021年第4期15卷 215-216页

作者： Elham MAZINAN Hassan NADERI Mitra MIRZAREZAEE Saber SAATI Department of Computer Engineering Science and Research BranchIslamic Azad UniversityTehran ***Iran Department of Computer Engineering Iran University of Science and TechnologyTehran ***Iran Department of Mathematics Tehran North branchIslamic Azad UniversityTehran ***Iran

1 Introduction and main contributions Everyone knows about the effectiveness of microblog social recommender systems,such as Twitter,in sharing information[1–3]One of the challenges posed by social recommender systems is the offering of relevant recommendations to the target user in spite of the heterogeneity of the links in the social ***,the mere consideration of links cannot offer proper recommendations to the target user.

关键词： similarity offering spite

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Efficient factored gradient descent algorithm for quantum state tomography

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Physical Review research 2024年第3期6卷 033034-033034页

作者： Yong Wang Lijun Liu Shuming Cheng Li Li Jie Chen College of Electronics and Information Engineering Department of Mathematics and Computer Science Shanghai Research Institute for Intelligent Autonomous Systems Institute for Advanced Study

Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute.

关键词： Quantum communication, protocols & technology Quantum engineering Quantum tomography

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Frilled Lizard Optimization: A Novel Bio-Inspired Optimizer for Solving engineering Applications

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computers, Materials & Continua 2024年第6期79卷 3631-3678页

作者： Ibraheem Abu Falahah Osama Al-Baik Saleh Alomari Gulnara Bektemyssova Saikat Gochhait Irina Leonova OmParkash Malik Frank Werner Mohammad Dehghani Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Software Engineering Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Science and Information Technology Software EngineeringJadara UniversityIrbid21110Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed UniversityPune412115India Neuroscience Research Institute Samara State Medical UniversitySamara443001Russia Faculty of Social Sciences Lobachevsky UniversityNizhny Novgorod603950Russia Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran

This research presents a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization(FLO),which emulates the unique hunting behavior of frilled lizards in their natural *** draws its inspiration from the sit-and-wait hunting strategy of these *** algorithm’s core principles are meticulously detailed and mathematically structured into two distinct phases:(i)an exploration phase,which mimics the lizard’s sudden attack on its prey,and(ii)an exploitation phase,which simulates the lizard’s retreat to the treetops after *** assess FLO’s efficacy in addressing optimization problems,its performance is rigorously tested on fifty-two standard benchmark *** functions include unimodal,high-dimensional multimodal,and fixed-dimensional multimodal functions,as well as the challenging CEC 2017 test ***’s performance is benchmarked against twelve established metaheuristic algorithms,providing a comprehensive comparative *** simulation results demonstrate that FLO excels in both exploration and exploitation,effectively balancing these two critical aspects throughout the search *** balanced approach enables FLO to outperform several competing algorithms in numerous test ***,FLO is applied to twenty-two constrained optimization problems from the CEC 2011 test suite and four complex engineering design problems,further validating its robustness and versatility in solving real-world optimization ***,the study highlights FLO’s superior performance and its potential as a powerful tool for tackling a wide range of optimization problems.

关键词： Optimization engineering bio-inspired metaheuristic frilled lizard exploration exploitation

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Evaluating the Performance of Subjective Weighting Methods for Multi-Criteria Decision-Making using a novel Weights Similarity Coefficient 27

Evaluating the Performance of Subjective Weighting Methods f...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Shekhovtsov, Andrii Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

In every decision-making problem which involves two or more criteria, there is to identify the relative importance of those criteria in order to make a proper decision. Very often, a decision-makers employee, for this purpose, subjective weighting methods, such as Analytic Hierarchy Process (AHP) or RANking COMparison (RANCOM). However, there is no simple way to compare the quality of the weights identification using different methods. To address this issue, this paper proposes a simple but efficient Weights Similarity Coefficient, which allows us to evaluate the relative performance of different subjective weighting methods. Furthermore, we propose a framework that utilizes the proposed coefficient in order to provide more complete comparison results. To demonstrate the applicability and effectiveness of the proposed framework, a case study is performed to compare two popular weighting methods: AHP and RANCOM. The results of the comparison prove that proposed coefficient with the combination of the proposed framework is an efficient instrument for weighing methods comparison. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： AHP Criteria weights Manhattan distance MCDA RANCOM SPOTIS

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