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Enhancing link prediction in graph data augmentation through graphon mixup

Neural Computing and Applications 2025年第8期37卷 6267-6282页

作者： Sultana, Tangina Hossain, Md. Delowar Morshed, Md. Golam Lee, Young-Koo Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Computer Science and Engineering Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh

Link prediction in complex networks is a fundamental problem with applications in diverse domains, from social networks to biological systems. Traditional approaches often struggle to capture intricate relationships in graphs, leading to suboptimal predictions. To address this, we introduce a novel method called graphon mixup (GM), which leverages the power of graphons to enhance link prediction. The augmentation strategy involves generating a synthetic graph by combining the original graph with a graphon-based synthetic graph. This process, expressed as a weighted combination of adjacency matrices, strategically blends real and synthetic information, enriching the training dataset. GM formulates link prediction as a joint optimization problem, aligning the characteristics of the synthetic graph with the true underlying structure. The objective is to minimize cross-entropy loss between predicted and true edge probabilities. A detailed computational complexity analysis evaluates the time and space requirements, aiding in understanding the efficiency and scalability of GM across different datasets and network sizes. Empirical validation on benchmark datasets demonstrates GM’s effectiveness in consistently improving average precision across diverse network types. The proposed method enhances the generalization capabilities of link prediction models, providing a more robust framework capable of accurate predictions even in the presence of noise or unseen patterns. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning Techniques 7

IUST at ClimateActivism 2024: Towards Optimal Stance Detecti...

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7th Workshop on Challenges and Applications of Automated Extraction of Socio-Political Events from Text, CASE 2024

作者： Mahmoudi, Ghazaleh Eetemadi, Sauleh School of Computer Engineering Iran University of Science and Technology Iran

ISBN: (纸本)9798891760707

This paper describes the IUST submission for sub-task C of the Climate Activism Shared Task at The 7th CASE workshop at EACL 2024. This work presents a systematic search of various model architecture configurations and data cleaning methods. The study evaluates the impact of data cleaning methods on the obtained results. Additionally, we demonstrate that a combination of CNN and Encoder-only models such as BERTweet outperforms FNNs. Moreover, by utilizing data augmentation, we are able to overcome the challenge of data imbalance. Our best system achieves 74.47% F1-Score on the unseen test set, outperforming the baseline by 19.97% and ranked 3th among 19 participants. © 2024 Association for Computational Linguistics.

关键词： Cleaning

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A Comprehensive Survey on Advanced Persistent Threat (APT) Detection Techniques

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computers, Materials & Continua 2024年第8期80卷 2675-2719页

作者： Singamaneni Krishnapriya Sukhvinder Singh Department of Computer Science School of Engineering and TechnologyPondicherry UniversityKalapet605014India

The increase in number of people using the Internet leads to increased cyberattack *** Persistent Threats,or APTs,are among the most dangerous targeted *** attacks utilize various advanced tools and techniques for attacking targets with specific *** countries with advanced technologies,like the US,Russia,the UK,and India,are susceptible to this targeted *** is a sophisticated attack that involves multiple stages and specific ***,TTP(Tools,Techniques,and Procedures)involved in the APT attack are commonly new and developed by an attacker to evade the security ***,APTs are generally implemented in multiple *** one of the stages is detected,we may apply a defense mechanism for subsequent stages,leading to the entire APT attack *** detection at the early stage of APT and the prediction of the next step in the APT kill chain are ongoing *** survey paper will provide knowledge about APT attacks and their essential *** follows the case study of known APT attacks,which will give clear information about the APT attack process—in later sections,highlighting the various detection methods defined by different researchers along with the limitations of the *** used in this article comes from the various annual reports published by security experts and blogs and information released by the enterprise networks targeted by the attack.

关键词： Advanced persistent threats APT cyber security intrusion detection cyber attacks

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Maximizing aged photovoltaic array power:a computer modelling study

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Clean Energy 2024年第1期8卷 171-187页

作者： Fadi N.Sibai Electrical&Computer Engineering Department Gulf University for Science and TechnologyMishrefKuwait

Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch

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Slender loris optimization algorithm for solving optimization problems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8799-8810页

作者： Lakshmi, A. Vijaya Vaitheki, K. Joseph, K. Suresh Department of Computer Science School of Engineering and Technology Pondicherry University India

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. © 2024 – IOS Press. All rights reserved.

关键词： Benchmarking

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Modeling Task Engagement to Regulate Reinforcement Learning-based Decoding for Online Brain Control

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 606-614页

作者： Zhang, Xiang Shen, Xiang Wang, Yiwen Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Brain-Machine Interfaces (BMIs) offer significant promise for enabling paralyzed individuals to control external devices using their brain signals. One challenge is that during the online Brain Control (BC) process, subjects may not be completely immersed in the task, particularly when multiple steps are needed to achieve a goal. The decoder indiscriminately takes the less engaged trials as training data, which might decrease the decoding accuracy. In this paper, we propose an alternative kernel RL-based decoder that trains online with continuous parameter update. We model neural activity from the medial prefrontal cortex (mPFC), a reward-related brain region, to represent task engagement. This information is incorporated into a stochastic learning rate using an exponential model, which measures the relevancy of neural data. The proposed algorithm was evaluated in the experiment where rats performed a cursor-reaching BC task. We found the neural activities from mPFC contained the engagement information which was negatively correlated with trial response time. Moreover, compared to the RL method without task engagement modeling, our proposed method enhanced the training efficiency. It used half of the training data to achieve the same reconstruction accuracy of the cursor trajectory. The results demonstrate the potential of our RL framework for improving online brain control tasks. © 2016 IEEE.

关键词： Reinforcement learning

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A self-predictive diagnosis system of liver failure based on multilayer neural networks

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Multimedia Tools and Applications 2024年第36期83卷 83769-83788页

作者： Dashti, Fatemeh Ghaffari, Ali Seyfollahi, Ali Arasteh, Bahman Department of Engineering Tabriz Electricity Distribution Company Tabriz Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The lack of symptoms in the early stages of liver disease may cause wrong diagnosis of the disease by many doctors and endanger the health of patients. Therefore, earlier and more accurate diagnosis of liver problems is necessary for proper treatment and prevention of serious damage to this vital organ. We attempted to develop an intelligent system to detect liver failure using data mining and artificial neural networks (ANN), this approach considers all factors impacting patient identification and enhances the probability of success in diagnosing liver failure. We employ multilayer perceptron neural networks for diagnosing liver failure via a liver patient dataset (ILDP). The proposed approach using the backpropagation algorithm, improves the diagnosis rate, and predicts liver failure intelligently. The simulation and data analysis outputs revealed that the proposed method has 99.5% accuracy, 99.65% sensitivity, and 99.57% specificity, making it more accurate than Previous related methods. © The Author(s) 2024.

关键词： Multilayer neural networks

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Cognition2Vocation: meta-learning via ConvNets and continuous transformers

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Neural Computing and Applications 2024年第21期36卷 12935-12950页

作者： Kamran, Sara Hosseini, Saeid Esmailzadeh, Sayna Kangavari, Mohammad Reza Hua, Wen School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing The Hong Kong Polytechnic University Hong Kong

Estimating the suitability of individuals for a vocation via leveraging the knowledge within cognitive factors comes with numerous applications: employment resourcing, occupation counseling, and workload management. Accordingly, the enterprises aim to hire the most suitable person from a massive array of similar applicants, maximizing performance and minimizing the gap between strategic indicators and predefined targets. While cognitive factors signify the best-suited person from similarly skilled workers, inferring pertinent latent cues from noisy and growing social network contents is time-intensive. To tackle the challenges involved, we propose a framework that, on the one hand, extends influential features based on the correlations between cognitive cues and, on the other hand, leverages a novel continuous transformer to mitigate the overlapping and approximation issues in discrete modeling. Rather than relying on discrete patterns that may evolve frequently, we use continuous elements that include not only numerous aggregating components but also sense minor irregular fluctuations. In a hybrid manner, we fuse multiple base models to transfer a higher representation to the meta-learning unit, agglomerating outputs from gradient boosters and the ConvNets. The experimental results show that our proposed framework can outperform trending vocation estimation methods by 1.36% in F1-Score and approximately 1% in accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Knowledge management

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