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DevDynaP: A Dynamic Local Community Detection Algorithm

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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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 Novel Approach to Energy Optimization:Efficient Path Selection in Wireless Sensor Networks with Hybrid ANN

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computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad Salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri Salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan School of Information Science and Engineering NingboTech UniversityNingbo315100China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless Sensor Networks(WSNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of Sensor Nodes(SNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(MS)to mitigate the energy consumption problem through periodic network *** mobile sink(MS)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(MS)concept has emerged as a promising solution to the energy consumption problem in WSNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient MS path depending upon rendezvous points(RPs).The first algorithm optimizes the MS’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using MS scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless Sensor Networks(WSNs) mobile sink(MS) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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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...

引用

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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An effective binary dynamic grey wolf optimization algorithm for the 0-1 knapsack problem

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Multimedia Tools and Applications 2024年 1-33页

作者： Erdoğan, Feyza Karakoyun, Murat Gülcü, Şaban Department of Software Engineering Konya Technical University Konya Turkey Department of Computer Engineering Necmettin Erbakan University Konya Turkey

Metaheuristic algorithms are recommended and frequently used methods for solving optimization problems. Today, it has been adapted to many challenging problems and its successes have been identified. The grey wolf optimizer (GWO) is one of the most advanced metaheuristics. Because of the advantages it provides, GWO has been applied to solve many different problems. In this study, a new variant of GWO, the Binary Dynamic Grey Wolf Optimizer (BDGWO), is proposed for the solution of binary optimization problems. The main contributions of BDGWO compared to other binary GWO variants are that it uses the XOR bitwise operation to binarize and is based on the dynamic coefficient method developed to determine the effect of the three dominant wolves (alpha, beta, and delta) in the algorithm. BDGWO is a simple, feasible, and successful method that strikes a balance between local search and global search in solving binary optimization problems. To determine the success and accuracy of the proposed BDGWO, it was tested on the 0-1 knapsack problem (0-1 KP), which is classified as an NP-Hard problem. The BDGWO was compared with 17 different binary methods across a total of 55 data sets from three different studies published in the last four years. The Friedman test was applied to interpret the experimental results more easily and to evaluate the algorithm results statistically. As a result of the experiments, it has been proven that the BDGWO is an effective and successful method in accordance with its purpose. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optimization algorithms

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Ensemble-based Robust Model for Accurate Driving Range Estimation of EVs Leveraging Big Data 8

Ensemble-based Robust Model for Accurate Driving Range Estim...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Hosseini, Samira Yassine, Abdulsalam Akilan, Thangarajah Department of Electrical and Computer Engineering Canada Department of Software Engineering Canada Department of Software Engineering Lakehead University Thunder Bay Canada

ISBN: (纸本)9798350382150

In the emergence of greener transportation, electric vehicles (EVs) play an important role, where the accurate prediction of the driving range is pivotal for alleviating driver range anxiety, serving as a foundation for spatial planning, operational strategies, and efficient charging infrastructure management. This study addresses the challenge of limited driving range in EVs and introduces an ensemble-based machine learning (ML) model for predicting the vehicle’s driving range. By employing big data collected from the real world, this work investigates the appropriateness of various ML models, including extreme learning model (ELM), extreme gradient boosting (XGBoost), multiple linear regression (MLR), multilayer perceptron (MLP), deep MLP, random forests (RF), AdaBoost, and support vector regression (SVR) for the problem stated above. Extensive experimental analysis proves that the ensemble framework that exploits MLR and XGBoost predictors surmounts the existing solutions. It achieves an R2 score greater than 0.9 on both training and testing data subsets, exhibiting no overfitting issues, and boasting acceptable inference time. The findings offer a compelling solution for enhancing the estimation of EV driving range for practical applications. ©2024 IEEE.

关键词： Forecasting

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Context Awareness by Noise-Pattern Analysis of a Smart Factory

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computers, Materials & Continua 2023年第8期76卷 1497-1514页

作者： So-Yeon Lee Jihoon Park Dae-Young Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial *** of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training ***,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very ***,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw *** the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed *** efficient dataset was built formodel learning through a total of two steps of data *** addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.

关键词： Noise-pattern recognition context awareness deep learning fault detection smart factory

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Identification of software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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