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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Nkolongo, Franck Tshibanda Echchakoui, Said Mehdi, Adda Department of Mathematics Computer Science and Engineering University of Quebec at Rimouski Canada Canada

Topic modeling is widely used for analyzing large textual datasets, particularly in technology patent nomination. Traditional methods, such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF), often struggle with semantic comprehension, scalability, and adaptability. This study investigates Large Language Models (LLMs) to overcome these limitations. Leveraging their advanced language understanding, GPT models generate coherent and contextually relevant topics, outperforming conventional methods. They also excel in identifying emerging trends, enhancing patent nomination processes. Our findings demonstrate the potential of GPT-based approaches to streamline patent analysis and accelerate technological discovery. © 2025 Elsevier B.V.. All rights reserved.

关键词： Non-negative matrix factorization

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MOPSO-driven optimization for sustainable retrofitting: balancing time, cost, and environmental impacts

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Asian Journal of Civil engineering 2025年第5期26卷 2165-2178页

作者： Singh, Rekha Sangeeta, Viswanadham Manjunath, T.C. Singh, Prachi Bishoyi, Sasanka Sekhar Pati, Aditya Kumar Department of Civil Engineering Raj Kumar Goel Institute of Technology Ghaziabad India Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science & Engineering Rajarajeswari College of Engineering Bengaluru India Department of Civil Engineering Madhav Institute of Technology and Science Gwalior India Department of Mathematics NIST University Berhampur India Department of Mathematics Centurion University of Technology and Management Gajapati R. Sitapur India

Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) approach to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its performance with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： MOPSO Multi Objective optimization Sustainable construction practices Sustainable retrofitting Time-cost-environmental trade-off

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An Anchor-Aware Graph Autoencoder Fused with Gini Index Model for Link Prediction

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SN computer science 2024年第6期5卷 745页

作者： Kumar, Shambhu Bisht, Dinesh Jain, Arti Computer Science & amp Engineering and IT Jaypee Institute of Information Technology Department of Mathematics Jaypee Institute of Information Technology

Link prediction stands as a crucial network challenge, garnering attention over the past decade, with its significance heightened by the escalating volume of network data. In response to the pressing need for swift research focus, this study introduces an innovative approach—the Anchor-aware Graph Autoencoder integrated with the Gini Index (AGA-GI)—aimed at gathering data on the global placements of link nodes within the link prediction framework. The proposed methodology encompasses three key components: anchor points, node-to-anchor paths, and node embedding. Anchor points within the network are identified by leveraging the graph structure as an input. The determination of anchor positions involves computing the Gini indexes (GI) of nodes, leading to the generation of a candidate set of anchors. Typically, these anchor points are distributed across the network structure, facilitating substantial informational exchanges with other nodes. The location-based similarity approach computes the paths between anchor points and nodes. It identifies the shortest path, creating a node path information function that incorporates feature details and location similarity. The ultimate embedding representation of the node is then formed by amalgamating attributes, global location data, and neighbourhood structure through an auto-encoder learning methodology. The Residual Capsule Network (RCN) model acquires these node embeddings as input to learn the feature representation of nodes and transforms the link prediction problem into a classification task. The suggested (AGA-GI) model undergoes comparison with various existing models in the realm of link prediction. These models include Attributes for Link Prediction (SEAL), Embeddings, Subgraphs, Dual-Encoder graph embedding with Alignment (DEAL), Embeddings and Spectral Clustering (SC), Deep Walk (DW), Graph Auto-encoder (GAE), Variational Graph Autoencoders (VGAE), Graph Attention Network (GAT), and Graph Conversion Capsule Link (G

关键词： Capsule network Ideal Anchor Point Link prediction Path Estimation Portion Path information function Residual module Shortest path distance

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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A MODIFIED WEAK GALERKIN FINITE ELEMENTMETHOD FOR SINGULARLY PERTURBED PARABOLIC CONVECTION-DIFFUSION-REACTION PROBLEMS

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Journal of Computational mathematics 2023年第6期41卷 1246-1280页

作者： Suayip Toprakseven Fuzheng Gao Faculty of Engineering Department of Computer ScienceArtvin Coruh UniversityArtvin08100Turkey School of Mathematics Shandong UniversityJinan 250100China

In this work,a modified weak Galerkin finite element method is proposed for solving second order linear parabolic singularly perturbed convection-diffusion *** key feature of the proposed method is to replace the classical gradient and divergence operators by the modified weak gradient and modified divergence operators,*** apply the backward finite difference method in time and the modified weak Galerkin finite element method in space on uniform *** stability analyses are presented for both semi-discrete and fully-discrete modified weak Galerkin finite element *** order of convergences are obtained in suitable *** have achieved the same accuracy with the weak Galerkin method while the degrees of freedom are reduced in our *** numerical examples are presented to support the theoretical *** is theoretically and numerically shown that the method is quite stable.

关键词： The modified weak Galerkin finite element method Backward Euler method Parabolic convection-diffusion problems Error estimates

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Distributed stochastic model predictive control for energy dispatch with distributionally robust optimization

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Applied mathematics and Mechanics(English Edition) 2025年第2期46卷 323-340页

作者： Mengting LIN Bin LI C.C.ECATI School of Aeronautics and Astronautics Sichuan UniversityChengdu 610065China Department of Information Engineering Computer Science and MathematicsUniversity of L’AquilaL’Aquila 67100Italy

A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is *** ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the *** power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)*** the exchange of information and energy flow,each microgrid can achieve its local supply-demand ***,the closed-loop stability and recursive feasibility of the proposed algorithm are *** comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.

关键词： distributed stochastic model predictive control(DSMPC) distributionally robust optimization(DRO) islanded multi-microgrid energy dispatch strategy

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A retailer’s inventory model for deteriorating items under power pattern demand with shortages partially backlogged in both crisp and fuzzy environments

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International Journal of Industrial and Systems engineering 2025年第1期49卷 96-118页

作者： Patra, Sourav Kumar Paikray, Susanta Kumar Tripathy, Rudra Mohan Department of Mathematics Veer Surendra Sai University of Technology Odisha Burla768018 India School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India

An inventory predicament can be resolved with numerous techniques, starting from the trial-and-error manner of mathematical and simulation methods. Mathematical methods always serve as powerful tools for minimising total inventory costs. In this paper, we have considered a retailer’s inventory problem in order to determine an optimal strategy that minimises the total inventory cost under various constraints. Here, the constraints include constant deterioration, power-pattern demand, permissible shortages, partial backlog, different inventory costs, and inherent imprecision of various expenses concerning the current scenario. Subsequently, we develop the mathematical model of the problem together with its solving policy in a crisp as well as fuzzy environments. Moreover, we provide several numerical illustrations to validate our findings. Finally, we present several managerial insights for inventory managers based on the sensitivity analysis of associated parameters. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Sensitivity analysis

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Cost-aware cloud workflow scheduling using DRL and simulated annealing

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Digital Communications and Networks 2024年第6期10卷 1590-1599页

作者： Yan Gu Feng Cheng Lijie Yang Junhui Xu Xiaomin Chen Long Cheng School of Control and Computer Engineering North China Electric Power UniversityBeijing 100026China School of Mathematics Southwest Jiaotong UniversityChengdu 610032China Department of Computer Science University of ReadingReading RG66AHUK

Cloud workloads are highly dynamic and complex,making task scheduling in cloud computing a challenging *** several scheduling algorithms have been proposed in recent years,they are mainly designed to handle batch tasks and not well-suited for real-time *** address this issue,researchers have started exploring the use of Deep Reinforcement Learning(DRL).However,the existing models are limited in handling independent tasks and cannot process workflows,which are prevalent in cloud computing and consist of related *** this paper,we propose SA-DQN,a scheduling approach specifically designed for real-time cloud *** approach seamlessly integrates the Simulated Annealing(SA)algorithm and Deep Q-Network(DQN)*** SA algorithm is employed to determine an optimal execution order of subtasks in a cloud server,serving as a crucial feature of the task for the neural network to *** provide a detailed design of our approach and show that SA-DQN outperforms existing algorithms in terms of handling real-time cloud workflows through experimental results.

关键词： Cloud computing Deep reinforcement learning Simulated annealing algorithm Job scheduling Workflow

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Design and evaluation of Swift routing for payment channel network

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Blockchain(Research and Applications) 2024年第2期5卷 62-72页

作者： Neeraj Sharma Kalpesh Kapoor V.Anirudh Department of Computer Science and Engineering Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia Department of Mathematics Indian Institute of Technology GuwahatiGuwahati 781039AssamIndia

Payment Channel Networks(PCNs)are a promising alternative to improve the scalability of a blockchain network.A PCN employs off-chain micropayment channels that do not need a global block confirmation procedure,thereby sacrificing the ability to confirm transactions *** uses a routing algorithm to identify a path between two users who do not have a direct channel between them to settle a *** performance of most of the existing centralized path-finding algorithms does not scale with network *** rapid growth of Bitcoin PCN necessitates considering distributed ***,the existing decentralized algorithms suffer from resource *** present a decentralized routing algorithm,Swift,focusing on fee *** concept of a secret path is used to reduce the path length between a sender and a receiver to optimize the ***,we reduce a network structure into combinations of cycles to theoretically study fee optimization with changes in cloud *** secret path also helps in edge load sharing,which results in an improvement of *** routing achieves up to 21%and 63%in fee and throughput optimization,*** results from the simulations follow the trends identified in the theoretical analysis.

关键词： Blockchain Payment channel networks Bitcoin Lightning network Routing protocols Layer 2 protocols

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Energy Aware Routing in Wireless Sensor Networks for Agricultural Monitoring: A Deep Q-Network Based Framework

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SN computer science 2024年第7期5卷 804页

作者： Dey, Ranadeep Thakurta, Parag Kumar Guha Kar, Samarjit Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Mathematics National Institute of Technology Durgapur India

Instead of earlier traditional farming, wireless sensor networks (WSNs) can be effectively used in the precision agriculture to improve the farmer’s livelihood. Whereas, hierarchical routing based protocols in WSNs are beneficial in agricultural monitoring as the network transmission load towards the sink is reduced. It decreases the energy requirement for routing the data. Following this, a deep Q-network (DQN) based energy aware multi-path routing of data towards the sink node in the WSNs is proposed in this paper. It improves the energy efficiency of sensor nodes, which in turn can eventually increase the network lifetime. Employing the proposed energy efficient routing technique, effective monitoring of the soil-moisture data in an agricultural field is taken into account. Initially, the soil moisture data from different depths of an agricultural field is collected, and it is then forwarded to an aggregator node. The principle component analysis is used for the data aggregation to obtain accurate correlated data with low power requirements. Finally, the aggregated data is routed from the aggregator node to the network gateway via the sink nodes in an energy efficient manner. The proposed DQN based routing uses the Q-table to store the Q-values for different state-action pairs. The high and positive Q-value of DQN is determined by the selection of better state-action pair. Various simulation results show that 3% saving of average residual energy is obtained by the proposed work over the existing approaches, which leads in approximately 12% increased network lifetime to route the data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep Q-network Energy efficiency Lifetime Precision agriculture Soil-moisture WSNs

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