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Secure information embedding in images with hybrid firefly algorithm

Neural Computing and Applications 2024年 1-14页

作者： Nokhwal, Sahil Chandrasekharan, Manoj Chaudhary, Ankit Department of Computer Science University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States School of Engineering Jawaharlal Nehru University Delhi New Delhi India

Various methods have been proposed to secure access to sensitive information over time, such as many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a secret. Multiple techniques that are commonly discussed for such scenarios suffer from low capacity and high distortion in the output signal. This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image by employing the hybrid firefly algorithm (HFA) proposed to select the pixel arrangement. This algorithm combines two widely used optimization algorithms to improve their performance. The suggested methodology utilizes the HFA algorithm to conduct a search for optimal pixel placements in the spatial domain. The purpose of this search is to accomplish two main goals: increasing the host image’s capacity and reducing distortion. Moreover, the proposed approach intends to reduce the time required for the embedding procedure. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process. The resultant embeddings exhibit robustness against steganalytic assaults, hence rendering the identification of the embedded data, a formidable undertaking. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Steganography

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Impact of Fast Frequency Response on Renewable Energy Source Curtailment and Load Shedding

Impact of Fast Frequency Response on Renewable Energy Source...

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CIRED 2024 Vienna Workshop

作者： Panagi, Savvas Lazari, Antonis Koutsoloukas, Vrahimis Aristidou, Petros Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Transmission System Operator of Cyprus Cyprus

ISBN: (纸本)9781837242672

System operators in low-inertia power systems often have to curtail renewable energy sources (RES) and employ strict under-frequency load shedding (UFLS) schemes to ensure frequency security after an event leading to loss of generation. This approach limits the maximum RES penetration in a system and results to the loss of load. To tackle these problems, fast frequency response (FFR) schemes can be used to limit the frequency Nadir after a disturbance and decrease the need for RES curtailments and UFLS. This article provides insights on the interaction between the Kinetic Energy (KE), Frequency Containment Reserves (FCR), and Nadir after a disturbance, which are the driving mechanisms leading to RES curtailment. Then, it analyses the impact of FFR on the Nadir and its ability to alleviate RES curtailment problems. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and showcase the impact on a real system. © The Institution of engineering & Technology 2024.

关键词： Electric load shedding

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Training neuro-fuzzy using flower pollination algorithm to predict number of COVID-19 cases: situation analysis for twenty countries

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Neural Computing and Applications 2024年第26期36卷 16365-16393页

作者： Baştemur Kaya, Ceren Kaya, Ebubekir Department of Computer Technologies Nevsehir Vocational School Nevsehir Haci Bektas Veli University Nevşehir Turkey Department of Computer Engineering Engineering Architecture Faculty Nevsehir Haci Bektas Veli University Nevşehir Turkey

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers. © The Author(s) 2024.

关键词： COVID-19

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Occupant-Centric Proactive Control Methodology for IEQ 18

Occupant-Centric Proactive Control Methodology for IEQ

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18th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2024

作者： Kakoulli, Christina Kyriacou, Alexis Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Lelantus Innovations Ltd Nicosia Cyprus

ISBN: (纸本)9798331306816

Indoor Environmental Quality (IEQ) is undeniably a crucial factor that affects the occupants’ overall health and well-being. However, there is a significant lack of available actionable and measurable real-time metrics for the impact of IEQ on occupants, restricting potential control solutions and the validation of their effectiveness. Even more, occupant-centric and energy concerned automatic control solutions with direct aim to decrease or even prevent any negative effects of poor IEQ on occupants are required for creating sustainable built environments. In this work, we propose a novel framework for an occupant-centric proactive air-purification and ventilation control methodology (PPV). The method utilizes state-space models along with what-if scenarios to generate the potential solution space. A multi-objective optimization extracts the optimal solution in terms of air-purification and ventilation operation points, while minimizing various computed IEQ impact metrics in terms of the occupants’ health, comfort and virus transmissibility risk, and also considering the energy expenditure. © 2024 18th Conference of the International Society of Indoor Air Quality and Climate, INDOOR AIR 2024 - Conference Program and Proceedings. All rights reserved.

关键词： Indoor air pollution

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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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