-
A multi-intent-aware recommendation algorithm based on interactive graph convolutional networks Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-15 Junsan Zhang, Hui Gao, Sen Xiao, Jie Zhu, Jian Wang
-
Human face identification after plastic surgery using SURF, Multi-KNN and BPNN techniques Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-13 Tanupreet Sabharwal, Rashmi Gupta
-
A graph neural network with negative message passing and uniformity maximization for graph coloring Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-13 Xiangyu Wang, Xueming Yan, Yaochu Jin
-
Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2024-03-15 Lotfi Mhamdi, Mohd Mat Isa
Software Defined Networking (SDN) has revolutionized network administration by providing centralized management through software, enabling traffic adjustment independent of the data plane. Despite the benefits, SDN networks are prone to security threats from external sources, thus necessitating the implementation of security measures. Unfortunately, most existing efforts have been just a simple mapping
-
Reliable federated learning based on dual-reputation reverse auction mechanism in Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-15 Yuncan Tang, Yongquan Liang, Yang Liu, Jinquan Zhang, Lina Ni, Liang Qi
Federated learning, a promising distributed machine learning paradigm, has been used in various Internet of Things (IoT) environments to solve isolated data island issues and protect data privacy. However, since the central server in federated learning cannot detect the local training process of the client, it is vulnerable to adversarial attacks against its security and privacy by malicious clients
-
Quality Prediction Modeling for Industrial Processes Using Multiscale Attention-Based Convolutional Neural Network IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-14 Xiaofeng Yuan, Lingfeng Huang, Lingjian Ye, Yalin Wang, Kai Wang, Chunhua Yang, Weihua Gui, Feifan Shen
-
Enhanced Chinese named entity recognition with multi-granularity BERT adapter and efficient global pointer Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-12
Abstract Named Entity Recognition (NER) plays a crucial role in the field of Natural Language Processing, holding significant value in applications such as information extraction, knowledge graphs, and question–answering systems. However, Chinese NER faces challenges such as semantic complexity, uncertain entity boundaries, and nested structures. To address these issues, this study proposes an innovative
-
Exponential Boundary Control for 2-D Spatial Distributed Parameter Systems Under Boundary Collocated and Planar Linear Measurements IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-13 Jingtao Man, Qiang Xiao, Zhigang Zeng
-
Digital twin framework for smart greenhouse management using next-gen mobile networks and machine learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Hameedur Rahman, Uzair Muzamil Shah, Syed Morsleen Riaz, Kashif Kifayat, Syed Atif Moqurrab, Joon Yoo
Due to the increase in world population, arable land has been reduced. Consequently, the concept of urban greenhouses is on the rise. Smart greenhouses need to monitor physical parameters for the healthy growth of plants from remote locations. A digital twin is a representation of physical assets in the digital world, and this emerging technology has opened up opportunities for efficient system development
-
Software Quality Assurance as a Service: Encompassing the quality assessment of software and services Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-13 Samuel Bernardo, Pablo Orviz, Mario David, Jorge Gomes, David Arce, Diana Naranjo, Ignacio Blanquer, Isabel Campos, Germán Moltó, Joao Pina
This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices
-
Robust Cooperative Control for Heterogeneous Uncertain Nonlinear High-Order Fully Actuated Multiagent Systems IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-12 Da-Wei Zhang, Guo-Ping Liu
-
Sliding Mode Control for Markov Jump Singularly Perturbed Systems Under Piecewise Homogeneous Stochastic Communication Protocol IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-12 Yanfang Tang, Xia Zhou, Ping Li, Jinde Cao, Jun Cheng
-
Hybrid learning of predictive mobile-edge computation offloading under differently-aged network states Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Chenshan Ren, Wei Song, Xinchen Lyu
By offloading computationally demanding applications to edge servers, mobile edge computing (MEC) can alleviate the stringent hardware requirements and save energy consumption of resource-restrained devices. Mobile edge computation offloading (MECO, i.e., optimizing computation offloading and resource allocation) is critical to the performance of MEC. However, the existing study typically assumed the
-
Multi-GPU work sharing in a task-based dataflow programming model Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-12 Joseph John, Josh Milthorpe, Thomas Herault, George Bosilca
Today, multi-GPU computing nodes are the mainstay of most high-performance computing systems. Despite significant progress in programmability, building an application that efficiently utilizes all the GPUs in a computing node is still a significant challenge, especially using the existing shared-memory and message-passing paradigms. In this aspect, the task-based dataflow programming model has emerged
-
A prediction method for dynamic multiobjective optimization based on joint subspace and correlation alignment Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-11 Guoping Li, Yanmin Liu, Xicai Deng
-
Toward automatic robotic massage based on interactive trajectory planning and control Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-09 Qinling Xu, Zhen Deng, Chao Zeng, Zhuoran Li, Bingwei He, Jianwei Zhang
-
Group decision on rationalizing disease analysis using novel distance measure on Pythagorean fuzziness Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-09 B. Baranidharan, Jie Liu, G. S. Mahapatra, B. S. Mahapatra, R. Srilalithambigai
-
Lightweight camouflaged object detection model based on multilevel feature fusion Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-09 Qiaoyi Li, Zhengjie Wang, Xiaoning Zhang, Hongbao Du
-
A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-11 Runqi Chai, Kaiyuan Chen, Bikang Hua, Yaoyao Lu, Yuanqing Xia, Xi-Ming Sun, Guo-Ping Liu, Wannian Liang
-
A Novel Dual-Phase Based Approach for Distributed Event-Triggered Control of Multiagent Systems With Guaranteed Performance IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-11 Libei Sun, Xiucai Huang, Yongduan Song, Marios Polycarpou
-
PPSFL: Privacy-Preserving Split Federated Learning for heterogeneous data in edge-based Internet of Things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Jiali Zheng, Yixin Chen, Qijia Lai
With the rapid increase in the number of Internet of Things (IoT) devices and the amount of data they generate, the traditional cloud-based approach is gradually unable to meet the actual needs of many scenarios. Distributed collaborative machine learning (DCML) paradigms such as Federated Learning (FL) and Split Learning (SL) provide possibilities for effective use of decentralized data in edge-based
-
Uncertainty-aware autonomous sensing with deep reinforcement learning Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Abdulmajid Murad, Frank Alexander Kraemer, Kerstin Bach, Gavin Taylor
Constructing an accurate representation model of phenomena with fewer measurements is a fundamental challenge in the Internet of Things. Leveraging sparse sensing policies to select the most informative measurements is a prominent technique for addressing resource constraints. However, designing such sensing policies requires significant domain knowledge and involves manually fine-tuned heuristics
-
Smartgrid-based hybrid digital twins framework for demand side recommendation service provision in distributed power systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-11 Abiodun E. Onile, Eduard Petlenkov, Yoash Levron, Juri Belikov
Electricity consumers face challenges in selecting an optimal energy-saving plan, and this is a sustainability problem. To set consumers focus on sustainable energy management, developments around ”Industry 4.0” are needed to achieve an optimal balance between cost and energy consumption with a focus on cutting-edge machine-learning models and smart services introduction. Energy modelling is crucial
-
Distributed State Estimation for Linear Systems in Networks With Antagonistic Interactions IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-07 Rui Gao, Guang-Hong Yang
-
AGF-Net: adaptive global feature fusion network for road extraction from remote-sensing images Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-05
Abstract Road extraction from remote-sensing images is of great significance for vehicle navigation and emergency insurance. However, the road information extracted in the remote-sensing image is discontinuous because the road in the image is often obscured by the shadows of trees or buildings. Moreover, due to the scale difference of roads in remote-sensing images, it remains a computational challenge
-
Knowledge-guided evolutionary algorithm for multi-satellite resource scheduling optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-06 Xingyi Yao, Xiaogang Pan, Tao Zhang, Wenhua Li, Jianjiang Wang
The Multi-Satellite Resource Scheduling Optimization Problem (MSRSOP) represents a complex optimization challenge, focusing on the allocation of limited ground tracking resources to satellite Tracking, Telemetry, and Command (TT&C) tasks, each with complex requirements. This paper introduces a novel mathematical model and a Knowledge-guided Evolutionary Algorithm (KgEA) tailored for the MSRSOP. Our
-
SRNN-RSA: a new method to solving time-dependent shortest path problems based on structural recurrent neural network and ripple spreading algorithm Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-05 Shilin Yu, Yuantao Song
-
A predictive-reactive strategy for flight test task scheduling with aircraft grounding Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-05 Bei Tian, Gang Xiao, Yu Shen
-
Covering-based $$(\alpha , \beta )$$ -multi-granulation bipolar fuzzy rough set model under bipolar fuzzy preference relation with decision-making applications Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-05 Rizwan Gul, Muhammad Shabir, Ahmad N. Al-Kenani
-
A prefetching indexing scheme for in-memory database systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Qian Zhang, Haoyun Song, Kaiyan Zhou, Jianhao Wei, Chuqiao Xiao
In-memory databases (IMDBs) store all working data in the main memory, making memory access the dominant factor in system performance. Moreover, for modern multi-version systems, the extended version chain makes the access pattern more complex, putting extra pressure on indexing. Our micro-architectural profiling results of existing IMDB indexing schemes show that over half of the execution time goes
-
DAG-aware harmonizing job scheduling and data caching for disaggregated analytics frameworks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Yulai Tong, Jiazhen Liu, Hua Wang, Mingjian He, Ke Zhou, Rongfeng He, Qin Zhang, Cheng Wang
Modern data analytics frameworks often integrate with external storage services, which can lead to storage bottlenecks. Existing caching and prefetching solutions utilize high-level information from data analytics frameworks to forecast future data accesses. They employ these predictions to prefetch data into the cache and manage the cache contents. However, this approach overlooks a fundamental opportunity:
-
CoPiFL: A collusion-resistant and privacy-preserving federated learning crowdsourcing scheme using blockchain and homomorphic encryption Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-05 Ruoting Xiong, Wei Ren, Shenghui Zhao, Jie He, Yi Ren, Kim-Kwang Raymond Choo, Geyong Min
Federated learning (FL) is one of many tasks facilitated by crowdsourcing. Generally in such a setting, participating workers cooperate to train a comprehensive model by exchanging the trained parameters. While blockchain-based crowdsourcing approaches offer advantages such as data integrity and tamper-proof properties, platform designers must also address potential risks such as data leakage, de-anonymization
-
An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04 Fatemeh Bakhshi, Mehrdad Ashtiani
-
STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04 Seong Jin Bang, Min Jung Kang, Min-Goo Lee, Sang Min Lee
-
The fuzzy support vector data description based on tightness for noisy label detection Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04
Abstract Machine learning (ML) is an approach driven by data, and as research in machine learning progresses, the issue of noisy labels has garnered widespread attention. Noisy labels can significantly reduce the accuracy of supervised classification models, making it important to address this problem. Therefore, it is a very meaningful task to detect as many noisy labels as possible from the big data
-
Cytopathology image analysis method based on high-resolution medical representation learning in medical decision-making system Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04 Baotian Li, Feng Liu, Baolong Lv, Yongjun Zhang, Fangfang Gou, Jia Wu
-
HoloSLAM: a novel approach to virtual landmark-based SLAM for indoor environments Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04 Elfituri S. Lahemer, Ahmad Rad
-
A lightweight contour detection network inspired by biology Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-04 Chuan Lin, Zhenguang Zhang, Jiansheng Peng, Fuzhang Li, Yongcai Pan, Yuwei Zhang
-
ELA-RCP: An energy-efficient and load balanced algorithm for reliable controller placement in software-defined networks J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2024-03-04 Maedeh Abedini Bagha, Kambiz Majidzadeh, Mohammad Masdari, Yousef Farhang
Software-defined networking is a modern and popular network technology that utilizes controllers as network operating systems to manage the network and allow user applications to interact with network hardware. Determining the optimal number of controllers and the optimal place to install them are some of the multi-controller model challenges that are known as the Controller Placement Problem (CPP)
-
Load-aware task migration algorithm toward adaptive load balancing in Edge Computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Xikang Zhu, Wenbin Yao, Wenhao Wang
The rapid advancement of the Internet of Things (IoT) is leading to more and more devices joining the network to interact with information, which requires improving the performance of IoT applications to accommodate more data, faster response times, and more complex tasks. Edge computing, as a new computing paradigm, brings resource contention and load imbalance while reducing communication overhead
-
EdgeOptimizer: A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Yufei Qiao, Shihao Shen, Cheng Zhang, Wenyu Wang, Tie Qiu, Xiaofei Wang
Edge computing has garnered significant attention in recent years, leading to the evolution of more delay-sensitive applications towards a three-tier architecture with edge-cloud collaboration. Concurrently, technologies associated with containerization have been maturing. Notably, (Kubernetes) emerges as a prominent solution for the management of extensive, dynamically evolving, and intricate container
-
SLA-based task offloading for energy consumption constrained workflows in fog computing Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Hongjian Li, Xue Zhang, Hua Li, Xiaolin Duan, Chen Xu
As an emerging computing paradigm, fog computing provides more available computing resources for Internet of Things (IoT) users in an efficient and timely manner. However, the energy consumption generated by fog computing is also further increased, which makes electricity costs and carbon emissions continue to rise. At the same time, the mobile characteristics of computing nodes in fog computing will
-
Lightweight verifiable blockchain top-[formula omitted] queries Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Jingxian Cheng, Saiyu Qi, Bochao An, Yong Qi, Jianfeng Wang, Yanan Qiao
Blockchain has been exploited in many applications as a fundamental technology to construct trust and share data among multiple participants. A user with limited resources who runs a light node fetches data records stored on the blockchain by requesting a full node that maintains the complete blockchain data. As a type of broadly used query, top- queries which ask for data records with the highest
-
Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-04 Wassim Boudieb, Abdelhamid Malki, Mimoun Malki, Ahmed Badawy, Mahmoud Barhamgi
Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart
-
Time-aware MADDPG with LSTM for multi-agent obstacle avoidance: a comparative study Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-02
Abstract Intelligent agents and multi-agent systems are increasingly used in complex scenarios, such as controlling groups of drones and non-player characters in video games. In these applications, multi-agent navigation and obstacle avoidance are foundational functions. However, problems become more challenging with the increased complexity of the environment and the dynamic decision-making interactions
-
A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-02 Youkai Xiao, Wei Du, Yang Tang
-
A data decomposition and attention mechanism-based hybrid approach for electricity load forecasting Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-02 Hadi Oqaibi, Jatin Bedi
-
Flexible job-shop scheduling problem with parallel batch machines based on an enhanced multi-population genetic algorithm Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-02 Lirui Xue, Shinan Zhao, Amin Mahmoudi, Mohammad Reza Feylizadeh
-
Predicting ride-hailing passenger demand: A POI-based adaptive clustering federated learning approach Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-03 Zhuhua Liao, Shoubin Li, Yijiang Zhao, Yizhi Liu, Wei Liang, Shaohua Wan
Passenger demand prediction is a key task for online ride-hailing platforms to optimize their resource allocation and service quality. However, centralized data collection and mining of massive passengers’ travel data expose serious privacy and security risks. To address this challenge, we propose a POI-based Adaptive Clustering Federated Learning with Spatio-Temporal Graph Attention Gate Recurrent
-
Throughput and delay analysis of cognitive M2M communications J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2024-03-02 Soumen Mondal, Luca Davoli, Sanjay Dhar Roy, Sumit Kundu, Gianluigi Ferrari, Riccardo Raheli
In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold
-
Discrete-time Markov decision process for performance analysis of virtual machine allocation schemes in C-RAN J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2024-03-02 Sana Younes, Maroua Idi, Riadh Robbana
Cloud Radio Access Network (C-RAN) has been proposed as a cloud architecture to provide a common connected resource pool management. It separates the functionalities of the traditional Base Station (BS) into two parts: the Base Band Unit (BBU) and the Remote Radio Head (RRH). BBUs functions are implemented on the Virtual Machines (VMs) in the cloud over commodity hardware, serving User Equipments (UEs)
-
ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Lingling Lu, Zhenyu Wen, Ye Yuan, Qinming He, Jianhai Chen, Zhenguang Liu
Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting
-
End-to-end network slicing for edge computing optimization Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Ahmet Cihat Baktır, Atay Özgövde, Cem Ersoy
User-centric services proliferated by the smart devices is getting more demanding and characteristically diversified. Fall-risk assessment, augmented reality and similar services coexist in a shared heterogeneous setting. To meet the diversified and often conflicting requirements of the services, the physical network is decomposed into virtual slices. In order to address the optimal network slicing
-
To store or not: Online cost optimization for running big data jobs on the cloud Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Xiankun Fu, Li Pan, Shijun Liu
As businesses increasingly rely on cloud-based big data analytics services to drive insights, reducing the cost of storing and analyzing large volumes of data in the cloud has become a major concern. During the execution of big data analysis jobs, some of the generated data can be reused by subsequent jobs. By storing such intermediate data, the cost of running big data jobs can be greatly reduced
-
Penetralium: Privacy-preserving and memory-efficient neural network inference at the edge Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Mengda Yang, Wenzhe Yi, Juan Wang, Hongxin Hu, Xiaoyang Xu, Ziang Li
The proliferation of artificial intelligence and edge computing has led to an increase in the deployment of proprietary deep learning models on third-party edge servers or devices to power mission-critical applications. However, this trend raises concerns about model privacy, particularly on untrusted edge platforms. Protecting model privacy in such scenarios requires addressing challenges such as
-
A fragmentation-aware redundancy elimination scheme for inline backup systems Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2024-03-02 Yucheng Zhang, Wenxuan Zhu, Dan Feng, Wei Huang, Nan Jiang, Meng Chen, Renxin Xia
Data deduplication is a widely employed technique in backup systems to enhance storage efficiency by eliminating duplicate chunks. Delta compression is a technique that complements deduplication by removing redundant data between similar chunks. However, when integrated into deduplication-based backup systems, delta compression can considerably decrease backup throughput due to the additional I/Os
-
Separating hard clean samples from noisy samples with samples’ learning risk for DNN when learning with noisy labels Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-01
Abstract Learning with Noisy Labels (LNL) methods aim to improve the accuracy of Deep Neural Networks (DNNs) when the training set contains samples with noisy or incorrect labels, and have become popular in recent years. Existing popular LNL methods frequently regard samples with high learning difficulty (high-loss and low prediction probability) as noisy samples; however, irregular feature patterns
-
Dynamic feedback algorithm based on spatial corner fitness for solving the three-dimensional multiple bin-size bin packing problem Complex Intell. Syst. (IF 5.8) Pub Date : 2024-03-01 Yi Liu, Xiaoyun Jiang
-
Robust Adaptive Fuzzy Control for Second-Order Euler–Lagrange Systems With Uncertainties and Disturbances via Nonlinear Negative-Imaginary Systems Theory IEEE Trans. Cybern. (IF 11.8) Pub Date : 2024-03-01 Vu Phi Tran, Mohamed A. Mabrok, Sreenatha G. Anavatti, Matthew A. Garratt, Ian R. Petersen
-
AI-enhanced blockchain technology: A review of advancements and opportunities J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2024-03-01 Dalila Ressi, Riccardo Romanello, Carla Piazza, Sabina Rossi
Blockchain technology has rapidly gained popularity, permeating various fields due to its inherent features of security, transparency, and decentralization. Blockchain-based applications, spanning from financial transactions to supply chain management, have revolutionized numerous industries. Concurrently, Artificial Intelligence (AI) techniques have emerged as a powerful tool for efficiently solving