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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

computer Modeling in engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software Engineering College of Computer Engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Q-Cogni: An Integrated Causal Reinforcement Learning Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6186-6195页

作者： Da Costa Cunha, Cristiano Liu, Wei French, Tim Mian, Ajmal University of Western Australia Department of Computer Science and Software Engineering PerthWA6009 Australia

We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning to improve the learning process with causal inference. Q-Cogni achieves improved policy quality and learning efficiency with a prelearned structural causal model of the environment, queried to guide the policy learning process with an understanding of cause-and-effect relationships in a state-action space. By doing so, we not only leverage the sample efficient techniques of reinforcement learning but also enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply Q-Cogni on vehicle routing problem (VRP) environments including a real-world dataset of taxis in New York City using the Taxi and Limousine Commission trip record data. We show Q-Cogni's capability to achieve an optimally guaranteed policy (total trip distance) in 76% of the cases when comparing to shortest-path-search methods and outperforming (shorter distances) state-of-the-art reinforcement learning algorithms in 66% of cases. Additionally, since Q-Cogni does not require a complete global map, we show that it can start efficiently routing with partial information and improve as more data is collected, such as traffic disruptions and changes in destination, making it ideal for deployment in real-world dynamic settings. © 2020 IEEE.

关键词： Adversarial machine learning

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Point cloud completion via structured feature maps using a feedback network

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Computational Visual Media 2023年第1期9卷 71-85页

作者： Zejia Su Haibin Huang Chongyang Ma Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhenChina Kuaishou Technology China

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature *** key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric *** accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local *** then integrate FSNet into a coarse-to-fine pipeline for point cloud ***,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point ***,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate *** efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point *** have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

关键词： 3D point clouds shape completion geometry processing deep learning

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DevDynaP: A Dynamic Local Community Detection Algorithm

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SN computer Science 2025年第4期6卷 1-11页

作者： Bakhtar, Sahar Harutyunyan, Hovhannes A. Department of Computer Science and Software Engineering Concordia University Montreal Canada

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. However, the problem of community detection in dynamic networks is well-investigated, the local community detection is not widely addressed by researchers in dynamic networks. In this paper, a dynamic local community detection algorithm is proposed. The purpose of the proposed algorithm is to explore the network as fast as possible and detect communities as well. Experimental results show that the proposed algorithm discovers the unknown parts of networks faster than similar algorithms. Also, the detected communities by the proposed algorithm outperform that of the compared algorithm in different snapshots. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dynamic social network Geodesic distance metric Local community detection Modularity

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Impact of optimizers functions on detection of Melanoma using transfer learning architectures

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Multimedia Tools and Applications 2025年第14期84卷 13787-13807页

作者： Kılıçarslan, Serhat Aydın, Hatice Aktas Adem, Kemal Yılmaz, Esra Kavalcı Software Engineering Bandırma Onyedi Eylul University Balıkesir Turkey Computer Engineering Sivas University of Science and Technology Sivas Turkey Computer Engineering Sivas Cumhuriyet University Sivas Turkey

Early diagnosis-treatment of melanoma is very important because of its dangerous nature and rapid spread. When diagnosed correctly and early, the recovery rate of patients increases significantly. Physical methods are not sufficient for diagnosis and classification. The aim of this study is to use a hybrid method that combines different deep learning methods in the classification of melanoma and to investigate the effect of optimizer methods used in deep learning methods on classification performance. In the study, Melanoma detection was carried out from the skin lesions image through a simulation created with the deep learning architectures DenseNet, InceptionV3, ResNet50, InceptionResNetV2 and MobileNet and seven optimizers: SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax and Nadam. The results of the study show that SGD has better and more stable performance in terms of convergence rate, training speed and performance than other optimizers. In addition, the momentum parameter added to the structure of the SGD optimizer reduces the oscillation and training time compared to other functions. It was observed that the best melanoma detection among the combined methods was achieved using the DenseNet model and SGD optimizer with a test accuracy of 0.949, test sensitivity 0.9403, and test F score 0.9492. © The Author(s) 2024.

关键词： Deep learning

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Data Aggregation-based Transmission Method in Ultra-Dense Wireless Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 727-737页

作者： Dae-Young Kim Seokhoon Kim Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

As the Internet of Things(IoT)advances,machine-type devices are densely deployed and massive networks such as ultra-dense networks(UDNs)are *** devices attend to the network to transmit data using machine-type communication(MTC),whereby numerous,various are *** devices generally have resource constraints and use wireless *** this kind of network,data aggregation is a key function to provide transmission *** can reduce the number of transmitted data in the network,and this leads to energy saving and reducing transmission *** order to effectively operate data aggregation in UDNs,it is important to select an aggregation point *** total number of transmitted data may vary,depending on the aggregation point to which the data are ***,in this paper,we propose a novel data aggregation scheme to select the appropriate aggregation point and describe the data transmission method applying the proposed aggregation *** addition,we evaluate the proposed scheme with extensive computer *** performances in the proposed scheme are achieved compared to the conventional approach.

关键词： Data aggregation data transmission ultra-dense network machine-type communication Internet of Things

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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A Review of Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches

A Review of Mental Health Analysis Through Social Media Usin...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Saleem, Maryam Afzal, Hammad Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Artificial intelligence has the potential to transform health care. For that purpose, machine learning (ML) and deep learning (DL) algorithms have been used in the prediction and diagnosis of many diseases. Many people across the world use social media platforms like Twitter, Facebook, Reddit, etc, more often to express their feelings. Mental health has become a big concern after the COVID-19 pandemic and many researchers have applied various ML and DL algorithms to social media data for mental health prediction and analysis. The purpose of this study is to provide a comprehensive analysis of ML and DL algorithms that have been used in the prediction of various mental disorders. This study provides a diverse review of 37 selected research papers. From the selected literature, an accuracy table of ML and DL algorithms for four mental disorders (Depression, Anxiety, Bipolar& ADHA) has been analyzed and created. This study aims to help researchers and practitioners in the future by providing a baseline of the performance of various ML and DL algorithms. Furthermore, we provide a list of publicly available datasets that will act as a knowledge base for future researchers. © 2024 IEEE.

关键词： Forecasting

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