Article
Multidisciplinary Sciences
Haifeng Hu, Zejun Sun, Feifei Wang, Liwen Zhang, Guan Wang
Summary: This paper proposes an accurate and efficient algorithm for critical node mining, which determines influential nodes using both global and local information. The proposed method solves the limitations of existing methods. Experimental results demonstrate that the algorithm effectively explores influential nodes in complex networks.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Information Systems
Umar Ishfaq, Hikmat Ullah Khan, Saqib Iqbal
Summary: Estimating the importance of nodes in complex social networks is crucial for understanding network robustness and stability. Existing centrality measures are based on single criteria, but this study proposes a novel centrality model that combines multiple measures for ranking nodes using entropy weighting and TOPSIS method.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Mathematics, Interdisciplinary Applications
Panfeng Liu, Longjie Li, Shiyu Fang, Yukai Yao
Summary: The study introduces a new influence maximization method VoteRank(++) which iteratively selects influential nodes through a voting approach, and outperforms baseline methods in spreading speed and infected scale in experiments.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Information Systems
Wafa Karoui, Nesrine Hafiene, Lotfi Ben Romdhane
Summary: This paper proposes a dynamic algorithm called DUIN, which effectively identifies and updates influential nodes in dynamic social networks, and successfully maximizes the influence propagation of nodes on both static and dynamic networks.
INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Farzaneh Kazemzadeh, Ali Asghar Safaei, Mitra Mirzarezaee, Sanaz Afsharian, Houman Kosarirad
Summary: With the increasing development of social networks, influence maximization has become an important research issue. In order to address the challenges of time efficiency and optimal selection of seed nodes, we propose the IMBC algorithm based on community structure. The algorithm improves time efficiency through optimal pruning and a minimum of dominating nodes, and selects seed nodes through scoring adjustment. Experimental results show that the algorithm outperforms other algorithms in influence spread and runtime.
Article
Mathematics, Interdisciplinary Applications
Yan Wang, Haozhan Li, Ling Zhang, Linlin Zhao, Wanlan Li
Summary: This article addresses the importance of identifying influential nodes in a network and proposes a new centrality measure and a heuristic algorithm for filtering propagators. Experimental results show that the new centrality measure is more accurate and effective, while the heuristic algorithm improves both spread speed and infection scale.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Multidisciplinary Sciences
Steven T. Smith, Edward K. Kao, Erika D. Mackin, Danelle C. Shah, Olga Simek, Donald B. Rubin
Summary: This paper presents an end-to-end framework to automate detection of disinformation campaigns, successfully applied to real-world hostile information operations in various countries. The system shows promising performance, able to accurately identify high-impact accounts that traditional statistical methods may miss.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Computer Science, Interdisciplinary Applications
Panfeng Liu, Longjie Li, Yanhong Wen, Shiyu Fang
Summary: The influence maximization (IM) problem aims to identify influential nodes in a network that can affect as many nodes as possible. VoteRank and its improved algorithms have been proposed to address this problem, but they do not allow self-voting, which is inconsistent with real scenarios. To tackle this issue, we designed the VoteRank* algorithm which introduces self-voting and considers node diversities. Experimental results on 12 benchmark networks demonstrate that VoteRank* outperforms baseline methods in most cases.
Article
Computer Science, Artificial Intelligence
K. Venkatakrishna Rao, Mahender Katukuri, Maheswari Jagarapu
Summary: The study focuses on identifying influential nodes in multilayer networks for efficient information spreading, proposing a novel clique-based influence maximization algorithm. Results show that the influence spread in multilayer networks is better with the proposed algorithm compared to others, demonstrating its effectiveness in detecting influential nodes.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Hardware & Architecture
Mohammed Bahutair, Zaher Al Aghbari, Ibrahim Kamel
Summary: The study introduces a new algorithm called NodeRank which ranks users in social networks based on both topological structure and user interests. Experiment results show that a parallel version using Hadoop Spark is more suitable than the MapReduce model for the algorithm.
JOURNAL OF SUPERCOMPUTING
(2022)
Article
Computer Science, Information Systems
Feiran Huang, Yang Yang, Zhigao Zheng, Guohua Wu, Shahid Mumtaz
Summary: The study focuses on identifying influential nodes in networks using an intelligent approach, and introduces a novel method to evaluate nodes based on observability and controllability. It is also important to detect and eliminate spammer nodes within the network. Experimental results demonstrate the superiority of the proposed approach in recognizing influential nodes.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Huan Li, Ruisheng Zhang, Zhili Zhao, Xin Liu, Yongna Yuan
Summary: In this study, a meta-heuristic discrete crow search algorithm (DCSA) is proposed to effectively solve the influence maximization problem. The algorithm utilizes a new coding mechanism, discrete evolution rules, degree-based initialization method, and random walk strategy to enhance search ability. Extensive experiments show that DCSA outperforms other algorithms in influence diffusion results under the independent cascade model.
APPLIED INTELLIGENCE
(2021)
Article
Telecommunications
Zufan Zhang, Xieliang Li, Chenquan Gan
Summary: This paper presents an influence maximization method based on community structure and influence distribution, utilizing network embedding for community detection and a greedy algorithm to identify influential seed nodes, with experimental results demonstrating its effectiveness and efficiency.
DIGITAL COMMUNICATIONS AND NETWORKS
(2021)
Article
Physics, Multidisciplinary
Asgarali Bouyer, Hamid Ahmadi Beni
Summary: The influence maximization problem is important in viral marketing for large-scale spreading in social networks. This paper proposes a fast and accurate method called the LMP algorithm, which uses node labeling and local traveling to select an optimized seed set. Experimental results show that the proposed algorithm is faster and achieves a good tradeoff between efficiency and time complexity.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Haotian Zhang, Shen Zhong, Yong Deng, Kang Hao Cheong
Summary: In this article, a novel centrality measure based on local fuzzy information centrality (LFIC) is proposed and its effectiveness is verified through multiple experiments. The results indicate that this method can identify influential nodes that cause wider scope of infection and larger effect on network connectivity. Furthermore, an extension method is proposed for weighted directed complex networks.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Dun Li, Dezhi Han, Noel Crespi, Roberto Minerva, Kuan-Ching Li
Summary: Supply chain finance provides credit for small and medium-sized enterprises with low credit lines and small financing scales. This study proposes a Blockchain-based secure storage system, Fabric-SCF, to achieve data security and fine-grained access control. Experimental results show that Fabric-SCF performs efficiently in a simulated real-world operating scenario.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Chemistry, Analytical
Saqib Majeed, Adnan Sohail, Kashif Naseer Qureshi, Saleem Iqbal, Ibrahim Tariq Javed, Noel Crespi, Wamda Nagmeldin, Abdelzahir Abdelmaboud
Summary: This paper proposes a coverage area decision model for UAV-BS in order to provide cellular network coverage using controlled and managed UAV nodes. The solution allows for real-time adjustment of node positions. Simulation results show that the proposed solution achieves better performance in network placement.
Article
Chemistry, Analytical
Raja Waseem Anwar, Kashif Naseer Qureshi, Wamda Nagmeldin, Abdelzahir Abdelmaboud, Kayhan Zrar Ghafoor, Ibrahim Tariq Javed, Noel Crespi
Summary: The concept of the Internet of Things (IoT) involves the connection of smart devices or objects to create a network. However, this new area faces challenges in handling big data and ensuring security. This paper proposes a data analytics model and self-organizing architecture for IoT networks, as well as a security model based on authentication, detection, and prediction mechanisms to enhance network security and protect against attacks.
Article
Chemistry, Multidisciplinary
Rajkumar Singh Rathore, Omprakash Kaiwartya, Kashif Naseer Qureshi, Ibrahim Tariq Javed, Wamda Nagmeldin, Abdelzahir Abdelmaboud, Noel Crespi
Summary: This paper presents a fault-tolerant and reliable green communications framework for next-generation wireless systems. By selecting node-disjoint routes and fault-tolerant routes, the performance of the framework is improved. Simulation experiments validate the efficacy of the framework.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Analytical
Fariba Ghaffari, Nischal Aryal, Emmanuel Bertin, Noel Crespi, Joaquin Garcia-Alfaro
Summary: Blockchain technology has witnessed a surge in new applications and initiatives to enhance decentralized security and trust by establishing new forms of relational reliance. This paper provides an overview of blockchain technology and its technological aspects, and presents a practical application of decentralized access control and pricing procedures in private cellular networks. The application facilitates the adoption of new business models by service and content providers, while addressing the scalability and operational complexity issues of conventional centralized access control systems. The design and implementation details of the proposed method in a real-world scenario using a private cellular network and blockchain technology are also provided.
Article
Computer Science, Artificial Intelligence
Zhenjiao Liu, Zhikui Chen, Yue Li, Liang Zhao, Tao Yang, Reza Farahbakhsh, Noel Crespi, Xiaodi Huang
Summary: This article presents a novel algorithm called IMC-NLT for incomplete multi-view clustering. By utilizing non-negative matrix factorization and a low-rank tensor, IMC-NLT effectively extracts hidden information from incomplete views, overcoming limitations of existing algorithms. Experimental results demonstrate that IMC-NLT outperforms baseline methods and produces stable and promising results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Meroua Moussaoui, Emmanuel Bertin, Noel Crespi
Summary: This article focuses on investigating the business models (BMs) needed to effectively address the opportunities promised by beyond-5G (B5G) and 6G networks, as traditional telecom roles may disappear or transform while new ones emerge. It highlights the major BM changes introduced by 5G in the telecommunications ecosystem, derives perspectives for B5G/6G BMs, and proposes business opportunities for the involved actors.
IEEE COMMUNICATIONS MAGAZINE
(2023)
Article
Computer Science, Artificial Intelligence
Wenhao Shao, Ruliang Xiao, Praboda Rajapaksha, Mengzhu Wang, Noel Crespi, Zhigang Luo, Roberto Minerva
Summary: A key challenge in video anomaly detection is identifying rare abnormal patterns in positive instances, which show small variation compared to normal patterns and are heavily influenced by dominant negative instances. To address this, we propose a weakly supervised video anomaly detection model called NTCN-ML - Novel Temporal Convolutional Network Multi-Instance Learning Model. The NTCN-ML model extracts temporal representations of video data to construct a time-series pattern and optimize the multi-instance learning process by examining the correlation between positive and negative samples to balance feature association between rare positive and negative instances. The video anomaly detection using the NTCN-ML model achieved 95.3% and 85.1% accuracy on the UCF-Crime and ShanghaiTech datasets respectively, outperforming the baseline models.
PATTERN RECOGNITION
(2023)
Article
Engineering, Electrical & Electronic
Aung Kaung Myat, Roberto Minerva, Attaphongse Taparugssanagorn, Praboda Rajapaksha, Noel Crespi
Summary: The conventional approaches for traffic intensity detection in smart cities use specialized sensors with high costs and limited reuse. This article proposes the utilization of general-purpose sensing with cost-effective and easily deployable sensors, such as microphones and air quality sensors. The authors demonstrate how noise signatures can be leveraged to measure traffic intensity using vision-transformers models. The vehicle type detection approach shows better results in traffic intensity detection.
IEEE SENSORS LETTERS
(2023)
Proceedings Paper
Computer Science, Information Systems
Syed Mohsan Raza, Roberto Minerva, Noel Crespi, Mehdi Karech
Summary: The telecommunications sector is exploring the use of Digital Twins to represent complex networks, which could harmonize different models of the Edge-Cloud Continuum. The Digital Twin Network (DTN) software framework aims to provide network operations with up-to-date and comprehensive views, along with simulating network behavior and learning from historical events. This work aims to consolidate a DTN data model representing the elements of the Edge-Cloud Continuum and bridge the telecommunications and Cloud industries.
2023 IEEE 9TH INTERNATIONAL CONFERENCE ON NETWORK SOFTWARIZATION, NETSOFT
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Fariba Ghaffari, Emmanuel Bertin, Noel Crespi
Summary: Mobile Number Portability (MNP) allows users to switch operators while keeping their number. However, current systems have various issues such as central point of failure, low scalability, high latency, and data leakage. To address these issues, a Blockchain-based solution is proposed to manage user subscription and profile porting, eliminating central points and providing increased automation, low latency, and high confidentiality.
2023 26TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS, ICIN
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Nischal Aryal, Emmanuel Bertin, Noel Crespi
Summary: Due to the inflexibility of existing RAN systems, the disaggregation of software and hardware can enable small-scale infrastructure providers to enter the market, leading to a more competitive ecosystem. Open Radio Access Network (O-RAN) builds a multi-vendor RAN ecosystem, addressing network complexity and providing cost-effective platforms. However, challenges like interoperability and AI/ML management need to be addressed for wide deployment. This paper surveys existing issues and explores solutions in the Open RAN ecosystem.
2023 26TH CONFERENCE ON INNOVATION IN CLOUDS, INTERNET AND NETWORKS AND WORKSHOPS, ICIN
(2023)
Proceedings Paper
Computer Science, Hardware & Architecture
Nischal Aryal, Fariba Ghaffari, Saeid Rezaei, Emmanuel Bertin, Noel Crespi
Summary: This paper presents the deployment procedure of a private 4G-LTE network with standard User Equipment in two different scenarios using OpenAirInterface and Magma core networks. It discusses the lessons learned from deploying the segregated end-to-end cellular network testbed, comparison of connection performance in two scenarios, challenges of connecting smartphones to the network, and comparison among the possible use-cases with each scenario.
2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022): INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Badr Bellaj, Aafaf Ouaddah, Emmanuel Bertin, Noel Crespi, Abdellatif Mezrioui
Summary: The success of Bitcoin and other cryptocurrencies has generated interest in Blockchain technology, but its evolution has led to diverging definitions and designs, making it difficult for designers and decision-makers to understand and choose suitable blockchain solutions.
INTELLIGENT COMPUTING, VOL 3
(2022)
Proceedings Paper
Engineering, Electrical & Electronic
Meroua Moussaoui, Emmanuel Bertin, Noel Crespi
Summary: This article provides a summary of the major shortcomings of 5G networks and classifies them into six primary domains. Additionally, it discusses the research challenges that will be faced in the development of future B5G and 6G networks.
2022 1ST INTERNATIONAL CONFERENCE ON 6G NETWORKING (6GNET)
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)