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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Evaluation of quality of service parameters for MQTT communication in IoT application by using deep neural network

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1123-1136页

作者： Pawar, Shital Jadhav, D.B. Lokhande, Meghana Raskar, Punam Patil, Megha Department of Computer Engineering Bharati Vidyapeeth’s College of Engineering for Women Pune India Department of Mechanical Engineering Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Department of Computer Engineering Smt. Kashibai Navale College of Engineering Pune India School of Mechatronics Engineering Symbiosis Skills and Professional University Pune India

The three main pillars of the Internet of Things (IoT) are Computation, Communication and things that are connected in a network of IoT. In IoT for communication, various protocols like Constrained Application Protocol (CoAP), Extensible Messaging and Presence Protocol (XMPP), Message Queuing Telemetry Transport (MQTT), etc. are used. As MQTT requires less battery power, has a small bandwidth, requires fewer resources and is easy to use, it is an ideal protocol for IoT. Hence it is commonly used in IoT. Recently the number of IoT devices has been increasing at a faster rate. Therefore, it is important to focus on the Quality of Service (QoS) of IoT applications and the smooth transmission of data over the network. QoS is one of the important elements in IoT systems that can be used for the evaluation of the performance and quality of IoT systems. The security breach in IoT will have a great impact on the performance of the IoT system. Therefore, it is essential to evaluate the QoS parameters. This research focuses on the secure data transmission in IoT applications using the MQTT protocol. The performance and security related QoS parameters of MQTT communication in IoT were evaluated and the security breaches were detected by using a neural network. The developed system provides better security and if still, any misactivity happens then it can also be detected. The developed system provides very good accuracy for the detection of security breaches. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Industrial IoT (IIoT) Machine-to-machine (M2M) MQTT MQTT broker QoS Security

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Adaptive multi-channel Bayesian graph attention network for IoT transaction security

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Digital Communications and Networks 2024年第3期10卷 631-644页

作者： Zhaowei Liu Dong Yang Shenqiang Wang Hang Su School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Electronics Information and BioengineeringPolitecnico di MilanoMilan20133Italy

With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic *** to the vast amounts of data created by numerous 5G IoT devices,the Ethereum platform has become a tool for the storage and sharing of IoT device data,thanks to its open and tamper-resistant ***,Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's *** modeling Ethereum trans-action records as a transaction network,the account types are well identified by the Ethereum account classifi-cation system established based on Graph Neural Networks(GNNs).This work first investigates the Ethereum transaction ***,experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per *** flaw may significantly impede the classification capability of GNNs,which is mostly governed by their *** work proposes an Adaptive Multi-channel Bayesian Graph Attention Network(AMBGAT)for Ethereum account clas-sification to address this *** uses attention to enhance node features,estimate graph topology that conforms to the ground truth,and efficiently extract node features pertinent to downstream *** extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

关键词： Internet of things Graph representation learning Node classification Security mechanism

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Smart Defense: Machine Learning-Based Web Application Firewall 7

Smart Defense: Machine Learning-Based Web Application Firewa...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Gupta, Kanan Kesarkar, Dhanashree P. Jadhav, Manasi M. Lagad, Sampada S. Korade, Nilesh B. Bhutnal, Vinodkumar JSPM's Rajarshi Shahu College of Engineering Computer Engineering Department Pune India Pimpri Chinchwad University School of Engineering and Technology Computer Engineering Department Pune India

ISBN: (纸本)9798331527822

The rise in internet usage has been revolutionary, as nearly 8 billion people in the world in 2024 - 5.35 billion of them, or around 66% of the world's population, are active internet users. These statistics clearly show that the development of web applications has skyrocketed as they offer accessibility and flexibility, but it also introduces serious threats that can inflict considerable damage on these applications and systems and compromise the personal information of their users. Hence, leveraging security mechanisms is equally crucial. In this research, an abnormality-based web application firewall model was developed using machine learning-based classification algorithms, mainly XG Boost. The performance of different classification-based algorithms was evaluated by developing different models. The proposed model achieves higher performance in differentiating between normal and malicious requests. As per investigation, our proposed model serves to be effective in differentiating between normal and malicious requests by delivering an accuracy level of 94.2%. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Interpolation Method for Missing Electricity Consumption Data

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computers, Materials & Continua 2024年第2期78卷 2575-2591页

作者： Junde Chen Jiajia Yuan Weirong Chen Adnan Zeb Md Suzauddola Yaser A.Nanehkaran Department of Electronic Commerce Xiangtan UniversityXiangtan411105China School of Information Engineering Yancheng Teachers UniversityYancheng224000China Department of Information and Electrical Engineering Ningde Normal UniversityNingde352100China College of Engineering Southern University of Science and TechnologyShenzhen518005China School of Informatics Xiamen UniversityXiamen361005China

Missing value is one of the main factors that cause dirty *** high-quality data,there will be no reliable analysis results and precise ***,the data warehouse needs to integrate high-quality data *** the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss *** the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity ***,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data *** missing data are randomly interpolated within the upper and lower ***,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption *** last,this process is implemented iteratively until the missing values do not *** a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete ***,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

关键词： Data interpolation GMDH electricity consumption data distribution system

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Deep Learning-Based Production Forecasting for Liquid-Rich Gas in the Duvernay Shale Play

Deep Learning-Based Production Forecasting for Liquid-Rich G...

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2025 SPE Canadian Energy Technology Conference and Exhibition, CET 2025

作者： Xu, Ziming Liu, Hongxuan Leung, Juliana Y. School of Mining & Petroleum Engineering Department of Civil & Environmental Engineering University of Alberta Canada Department of Computer Engineering University of Alberta Canada

ISBN: (纸本)9781959025672

The Duvernay Formation is one of the most significant unconventional hydrocarbon formations in the Western Canada Sedimentary Basin (WCSB), known for its high liquid hydrocarbon content. Due to hydraulic fracturing being widely applied, the significant reservoir heterogeneity makes forecasting the newly developed well extremely challenging compared to traditional methods. Our previous work successfully applied a deep learning-based production forecasting model to the Montney shale gas play. However, Duvernay shale play exhibits significant variability in gas and liquid production proportions across different regions. This variation introduces challenges in accurately predicting multi-phase flow production behaviour. This study enhances our previously developed Masked Encoding and Decoding (MED) architecture for forecasting multi-phase hydrocarbon production from the Duvernay Formation. To mitigate the accumulation of errors typically encountered in recursive generation methods for the three production phases (oil, gas, and water), the model adopts a Non-Autoregressive Generation (NAG) approach, which predicts future production in a single step. The model integrates geostatic properties and continuously updates as new production data becomes available. Experiments were conducted using a dataset of 2, 700 wells from the Duvernay Formation, with oil, gas, and water production rates preprocessed using a novel Arp's decline denoising method to enhance model stability during training. Results demonstrate the enhanced MED model's superior accuracy compared to other well-known sequence-to-sequence models, effectively capturing complex gas-liquid ratio variability and dynamically updating predictions with new data. Copyright 2025, Society of Petroleum Engineers

关键词： Hydraulic fracturing

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Energy Efficient Green Routing for UAVs Ad-Hoc Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 1111-1127页

作者： M.Muthukumar Rajasekar Rangasamy Irshad Hussain Salman A.AlQahtani Department of Computer Science and Engineering Sree Sowdambika College of EngineeringTamil NaduIndia Department of Computer Science and Engineering-AI&ML School of EngineeringMalla Reddy UniversityHyderabadIndia Faculty of Electrical and Computer Engineering University of Engineering and Technology Peshawar25000PeshawarPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia

The purpose of this article is to propose Stability-based Energy-Efficient Link-State Hybrid Routing(S-ELHR),a low latency routing proto-col that aims to provide a stable mechanism for routing in unmanned aerial vehicles(UAV).The S-ELHR protocol selects a number of network nodes to create a Connected Dominating Set(CDS)using a parameter known as the Stability Metric(SM).The SM considers the node’s energy usage,connectivity time,and node’s *** the highest SM nodes are chosen to form *** node declares a Willingness to indicate that it is prepared to serve as a relay for its neighbors,by employing its own energy state.S-ELHR is a hybrid protocol that stores only partial topological information and routing tables on CDS *** of relying on the routing information at each intermediary node,it uses source routing,in which a route is generated on-demand,and data packets contain the addresses of the nodes the packet will transit.A route recovery technique is additionally utilized,which first locates a new route to the destination before forwarding packets along *** simulation for various network sizes and mobility speeds,the efficiency of S-ELHR is *** findings demonstrate that S-ELHR performs better than Optimized Link State Routing(OLSR)and Energy Enhanced OLSR(EE-OLSR)in terms of packet delivery ratio,end-to-end delay,and energy consumption.

关键词： Connected dominatingset hybridrouting UAVAdhocnetworks link state stability

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Interpolated Bumpless Transfer Control for Asynchronously Switched Linear Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1579-1590页

作者： Shengao Lu Tong Wu Lixian Zhang Jianan Yang Ye Liang the School of Astronautics Harbin Institute of TechnologyHarbin 150080China the Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore IEEE

This paper revisits the problem of bumpless transfer control(BTC) for discrete-time nondeterministic switched linear systems. The general case of asynchronous switching is considered for the first time in the field of BTC for switched systems. A new approach called interpolated bumpless transfer control(IBTC) is proposed, where the bumpless transfer controllers are formulated with the combination of the two adjacent modedependent controller gains, and are interpolated for finite steps once the switching is detected. In contrast with the existing approaches, IBTC does not necessarily run through the full interval of subsystems, as well as possesses the time-varying controller gains(with more flexibility and less conservatism) achieved from a control synthesis allowing for the stability and other performance of the whole switched system. Sufficient conditions ensuring stability and H_(∞) performance of the underlying system by IBTC are developed, and numerical examples verify the theoretical findings.

关键词： Asynchronous switching bumpless transfer H_∞control switched systems

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A Bayesian Model Based on Link Distribution Features for Multi-target Passive Localization in Visible Light Sensing

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IEEE Sensors Journal 2025年第11期25卷 20037-20050页

作者： Zhang, Shuai Zhang, Liyi Liu, Kaihua Wang, Ya Tianjin University of Commerce School of Information engineering Tianjin300134 China Tianjin University School of Microelectronics Tianjin300072 China Tianjin Renai College Information Engineering Department Tianjin301636 China Texas A&M University Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Mechanical Engineering J. Mike Walker 66 College StationTX77843 United States

Passive localization using visible light sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multi-target positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shad-owed link information of candidate targets. Numerous simulation results show that the proposed multi-target passive localization scheme can improve the problem of false target localization in multi-target localization scenarios. And it also can achieve outstanding performance in localization accuracy. © 2001-2012 IEEE.

关键词： Sensors Location awareness Transmitters Light emitting diodes Accuracy Bayes methods Vectors Receivers RF signals Time measurement

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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