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Leader-follower green traffic assignment problem with online supervised machine learning solution approach

Soft Computing 2025年第1期29卷 103-116页

作者： Sadra, M. Zaferanieh, M. Yazdimoghaddam, J. Department of Mathematics and Computer Science Hakim Sabzevari University Sabzevar Iran Department of Industrial Engineering Hakim Sabzevari University Sabzevar Iran

In this paper, we propose a bi-level green traffic assignment with network design problem. At the upper level, the objective function evaluates a total traffic Carbon monoxide (CO) gas emissions problem to provide a macroscopic viewpoint of the system manager. At the lower-level, a traffic assignment network design problem is considered to individually optimize users’ travel times, with certain links being potential candidates for network addition. Although the lower-level objective function is convex with linear constraints, the proposed bi-level problem is np-hard, and even finding a near-optimal solution is an np-hard task. To address the solution approach, we applied an online supervised machine learning (SML) algorithm which solves the proposed bi-level problem within a reasonable running time. Additionally, a mat-heuristic algorithm is proposed to compare the results with the online SML algorithm. To validate the online SML algorithm, we conducted experiments using real urban transportation examples in medium and large-sized networks. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Travel time

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Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9491-9510页

作者： Madhubala, P. Ghanimi, Hayder M.A. Sengan, Sudhakar Abhishek, Kumar Department of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Tamil Nadu Salem India Information Technology Department College of Science University of Warith Al-Anbiyaa Karbala Iraq Computer Science Department College of Computer Science and Information Technology University of Kerbala Karbala Iraq Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mathematics VIT Bhopal Bhopal India

The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. © 2024 – IOS Press. All rights reserved.

关键词： Information retrieval

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Nonlinearity in Large-Cycle Cellular Automata

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Complex Systems 2025年第1期34卷 129-160页

作者： Mukherjee, Sukanya Adak, Sumit Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata700091 India Department of Applied Mathematics and Computer Science DTU Compute Technical University of Denmark Kgs. Lyngby2800 Denmark

The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2n-1 where n ∈ , a cycle of length l can be generated by a cellular automaton (CA) of size ⌊log2 l⌋ + 1;this maximal-length CA is in fact the optimal (minimum-sized) CA to generate a cycle of length l. However, maximal-length CAs are, in general, linear and have some drawbacks. Moreover, it is nontrivial to address this problem for any l ∈ , that is, removing the constraint that l is of the form 2n-1. In this paper we therefore employ nonlinearity in the rules and aim to generate the near-optimal CA for any given cycle length l, meaning that the generated CAs will be of size n = (⌊log2 l⌋ + 1) + d, where the displacement d is as small as possible. We use a generic operator to concatenate a collection of component CAs of sizes n1, n2, …, nk by employing an evolutionary strategy. Here, n = n1 + n2 + ⋯ + nk and l ≥ lcm(1, 2, …, k ), where i (1 ≤ i ≤ k) is the maximum cycle length of a CA of size ni . An influencing factor in the design of component CAs is to achieve the near-optimal CA for a given cycle length. Here we consider nonlinear reversible large-cycle CAs as component CAs. We also measure the effectiveness of nonlinearity in large-cycle generation. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： CAs cellular automata evolution large-cycle CA neighborhood dependent nonlinearity reversible CAs

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MD-HIT:Machine learning for material property prediction with dataset redundancy control

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npj Computational Materials 2024年第1期10卷 638-648页

作者： Qin Li Nihang Fu Sadman Sadeed Omee Jianjun Hu College of Big Data and Statistics Guizhou University of Finance and EconomicsGuiyangChina Department of Computer SCience and Engineering University of South CarolinaColumbiaSCUSA

Materials datasets usually contain many redundant(highly similar)materials due to the tinkering approach historically used in material *** redundancy skews the performance evaluation of machine learning(ML)models when using random splitting,leading to overestimated predictive performance and poor performance on out-of-distribution *** issue is well-known in bioinformatics for protein function prediction,where tools like CD-HIT are used to reduce redundancy by ensuring sequence similarity among samples greater than a given *** this paper,we survey the overestimated ML performance in materials science for material property prediction and propose MD-HIT,a redundancy reduction algorithm for material *** MD-HIT to composition-and structure-based formation energy and band gap prediction problems,we demonstrate that with redundancy control,the prediction performances of the ML models on test sets tend to have relatively lower performance compared to the model with high redundancy,but better reflect models’true prediction capability.

关键词： prediction property performance

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Explainable AI model for PDFMal detection based on gradient boosting model

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Neural Computing and Applications 2024年第34期36卷 21607-21622页

作者： Elattar, Mona Younes, Ahmed Gad, Ibrahim Elkabani, Islam Department of Computer Science Faculty of Science Tanta University Tanta31527 Egypt Department of Mathematics and Computer Science Faculty of Science Alexandria University Alexandria21511 Egypt Faculty of Computer Science and Engineering Al Alamein International University Alamein51718 Egypt

Portable document formats (PDFs) are widely used for document exchange due to their widespread usage and versatility. However, PDFs are highly vulnerable to malware attacks, which pose significant security risks. Existing defense mechanisms often struggle to effectively detect and mitigate these threats, highlighting the need for more robust solutions. This paper introduces a robust framework that uses advanced tree-based ensemble models to detect malicious PDFs using the Evasive-PDFMal2022 dataset. The proposed model achieves a recall rate of 100%, an accuracy rate of 99.95%, and a fast inference time of 0.1723 s. Furthermore, the framework exhibits minimal false positive and false negative rates, ensuring a high level of precision in distinguishing between malicious and benign PDFs. Shapley additive explanations are used to improve the interpretability and reliability of the model’s predictions. The results highlight the effectiveness of the proposed model in improving PDF document security and addressing the challenges posed by malware attacks. © The Author(s) 2024.

关键词： Explainable artificial intelligence (XAI) Malicious PDF Malware detection Tree-based ensemble models

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τSQWRL:A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

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Big Data Mining and Analytics 2023年第3期6卷 288-300页

作者： Zouhaier Brahmia Fabio Grandi Rafik Bouaziz Department of Computer Science Faculty of Economics and ManagementUniversity of SfaxSfax 3029Tunisia. Department of Computer Science and Engineering University of BolognaBologna 40136Italy.

Temporal ontologies allow to represent not only concepts,their properties,and their relationships,but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist *** are widely used to formally represent temporal data semantics in several applications belonging to different fields(e.g.,Semantic Web,expert systems,knowledge bases,big data,and artificial intelligence).They facilitate temporal knowledge representation and discovery,with the support of temporal data querying and ***,there is no standard or consensual temporal ontology query *** a previous work,we have proposed an approach namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL allows(1)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(2)to manage its incremental maintenance accommodating their evolution,in a temporal and multi-schema-version *** this paper,we propose a temporal ontology query language forτJOWL,namedτSQWRL(temporal SQWRL),designed as a temporal extension of the ontology query language—Semantic Query-enhanced Web Rule Language(SQWRL).The new language has been inspired by the features of the consensual temporal query language TSQL2(Temporal SQL2),well known in the temporal(relational)database *** aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any(complex)temporal query on time-varying ontologies generated from time-varying big *** examples,in the Internet of Healthcare Things(IoHT)domain,are provided to motivate and illustrate our proposal.

关键词： temporal ontology temporal big data temporal query language temporal OWL 2 from temporal JSON(τJOWL) Semantic Query-enhanced Web Rule Language(SQWRL) Temporal SQL2(TSQL2) Internet of Healthcare Things(IoHT)

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Generative artificial intelligence for distributed learning to enhance smart grid communication

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International Journal of Intelligent Networks 2024年第1期5卷 267-274页

作者： Sajjadi Mohammadabadi, Seyed Mahmoud Entezami, Mahmoudreza Karimi Moghaddam, Aidin Orangian, Mansour Nejadshamsi, Shayan University of Nevada Computer Science and Engineering Department RenoNV United States Concordia University Computer Science and Software Engineering Department MontrealQC Canada University of Zanjan Department of Mathematics and Computer Science Zanjan Iran Islamic Azad University South Tehran Branch Department of Engineering Tehran Iran MontrealQC Canada

Machine learning models are the backbone of smart grid optimization, but their effectiveness hinges on access to vast amounts of training data. However, smart grids face critical communication bottlenecks due to the ever-increasing volume of data from distributed sensors. This paper introduces a novel approach leveraging Generative Artificial Intelligence (GenAI), specifically a type of pre-trained Foundation Model (FM) architecture suitable for time series data due to its efficiency and privacy-preserving properties. These GenAI models are distributed to agents, or data holders, empowering them to fine-tune the foundation model with their local datasets. By fine-tuning the foundation model, the updated model can produce synthetic data that mirrors real-world grid conditions. The server aggregates fine-tuned model from all agents and then generates synthetic data which considers all data collected in the grid. This synthetic data can be used to train global machine learning models for specific tasks like anomaly detection and energy optimization. Then, the trained task models are distributed to agents in the grid to leverage them. The paper highlights the advantages of GenAI for smart grid communication, including reduced communication burden, enhanced privacy through anonymized data transmission, and improved efficiency and scalability. By enabling a distributed and intelligent communication architecture, GenAI introduces a novel way for a more secure, efficient, and sustainable energy future. © 2024 The Authors

关键词： Smart power grids

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Carbon information disclosure mechanism considering extended producer responsibility and low carbon preference under outsourcing remanufacturing

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Sustainable Operations and computers 2025年 6卷 71-84页

作者： Zhao, Xiukun Liu, Yajiao Zou, Jinhui Deng, Qianzhou Han, Rong School of Management Science and Engineering Tianjin University of Finance and Economics Tianjin300222 China College of Mathematics and Computer Science Zhejiang A&F University Hangzhou311300 China Business School Tianjin University of Finance and Economics Tianjin300222 China

The implementation of EPR information disclosure responsibility is one of the four key tasks in the implementation of EPR. In order to explore the impact mechanism of carbon information disclosure behavior of manufacturers and remanufacturers considering extended producer responsibility (EPR) and low-carbon preference on corporate profits of outsourced remanufacturing closed-loop supply chain, this study constructs a two-level supply chain composed of manufacturers and remanufacturers. This paper integrates corporate carbon disclosure with recycling and remanufacturing activities and develop four models according to whether enterprises fulfill EPR information disclosure responsibilities. This study analyzes the equilibrium strategies of manufacturers and remanufacturers in four modes through Stackelberg game and finds that: Firstly, the implementation of EPR is more conducive to enhancing the overall profitability of enterprises compared to a scenario without EPR. Concurrently, enterprises will focus more on consumer low-carbon preferences and market dynamics when implementing EPR policies, thereby striving to enhance their competitive edge. Secondly, manufacturers and remanufacturers should collaborate actively in carbon information disclosure activities to achieve coordinated development between upstream and downstream enterprises. Such collaboration is beneficial for increasing the product recovery rate of remanufacturers. Lastly, social welfare is maximized under a full EPR scenario. The state of social welfare varies with changes in cost subsidy disclosure and consumer low-carbon preferences. Notably, the disclosure efforts of remanufacturers contribute more significantly to social welfare than those of manufacturers. © 2025 The Authors

关键词： Extended producer responsibility Carbon information disclosure Outsourcing remanufacturing Low-carbon preference

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A Contribution to the Spatial Analysis of Territorial Systems Based on Graph-Structured Data

A Contribution to the Spatial Analysis of Territorial System...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Corrado, Simone Romaniello, Federico Scorza, Francesco Potenza Italy University of Basilicata Department of Mathematics Computer Science and Economics Potenza Italy

ISBN: (纸本)9798350385014

This work aims to present the methodological framework within which the T4Y project is developing as a contribution to the spatial analysis of complex systems related to tourism. The mapping of tourist ecosystems from a territorial perspective is a complex phenomenon due to the difficulty in understanding the interactions between tourists and the territory. Through the integration of AI-based techniques, the study aims to propose an approach based on graph-structured data to manage the complexity of geospatial information and improve planners' ability to abstract the possible relationships between elements of the territory. The approach utilizes graph-based data modeling while preserving spatial connections. It has been demonstrated how, through a bipartite graph approach, it is possible to decode the territorial components while taking into account the spatial connections between different pieces of information. Furthermore, the implementation of Graph Neural Networks (GNN) has been discussed as a way to facilitate a more integrated and context-aware analysis. This work aims to contribute to the ongoing debate on complex systems in spatial planning and provide valuable tools for policymakers and planners to experiment with scenarios for spatial development. © 2024 IEEE.

关键词： Graphic methods

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Multi-solutions with specific geometrical wave structures to a nonlinear evolution equation in the presence of the linear superposition principle

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Communications in Theoretical Physics 2023年第1期75卷 5-14页

作者： Hajar F Ismael Tukur Abdulkadir Sulaiman M S Osman Department of Mathematics Faculty of ScienceUniversity of ZakhoZakhoIraq Department of Computer Engineering Biruni UniversityIstanbulTurkey Department of Mathematics Faculty of ScienceCairo UniversityGiza 12613Egypt

Lump solutions are one of the most common solutions for nonlinear evolution *** study aspires to investigate the generalized Hietarintatype *** auspiciously provide multiple M-lump *** the other hand,collision phenomena to multiple M-lump waves with soliton wave solutions are also *** the collision,the amplitude of the lump will change significantly over the processes,whereas the amplitude of the soliton will just minimally *** it is of paramount importance,we use suitable values of parameter to put out the physical features of the reported results through three dimensional and contour *** results presented express physical features of lump and lump interaction phenomena of different kinds of nonlinear physical ***,this study serves to enrich nonlinear dynamics and provide insight into how nonlinear waves propagate.

关键词： the generalized Hietarintatype equation M-lump waves interaction phenomena hirota bilinear method

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