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Enhancing lung cancer diagnosis with data fusion and mobile edge computing using DenseNet and CNN J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-19 Chengping Zhang, Muhammad Aamir, Yurong Guan, Muna Al-Razgan, Emad Mahrous Awwad, Rizwan Ullah, Uzair Aslam Bhatti, Yazeed Yasin Ghadi
The recent advancements in automated lung cancer diagnosis through the application of Convolutional Neural Networks (CNN) on Computed Tomography (CT) scans have marked a significant leap in medical imaging and diagnostics. The precision of these CNN-based classifiers in detecting and analyzing lung cancer symptoms has opened new avenues in early detection and treatment planning. However, despite these
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Cross-chain asset trading scheme for notaries based on edge cloud storage J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-16 Lang Chen, Yuling Chen, Chaoyue Tan, Yun Luo, Hui Dou, Yuxiang Yang
Blockchain has penetrated in various fields, such as finance, healthcare, supply chain, and intelligent transportation, but the value exchange between different blockchains limits their expansion. Cross-chain technology, such as notary mechanism, enables asset exchanges between different blockchain networks. However, existing research still confronts problems such as single inherent value evaluation
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An overview of QoS-aware load balancing techniques in SDN-based IoT networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-13 Mohammad Rostami, Salman Goli-Bidgoli
Increasing and heterogeneous service demands have led to traffic increase, and load imbalance challenges among network entities in the Internet of Things (IoT) environments. It can affect Quality of Service (QoS) parameters. By separating the network control layer from the data layer, Software-Defined Networking (SDN) has drawn the interest of many researchers. Efficient data flow management and better
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MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-12 Veni Thangaraj, Thankaraja Raja Sree
Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications
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Correction to: Edge intelligence‑assisted animation design with large models: a survey J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-11 Jing Zhu, Chuanjiang Hu, Edris Khezri, Mohd Mustafa Mohd Ghazali
Correction to: Journal of Cloud Computing (2024) 13:48 https://doi.org/10.1186/s13677-024-00601-3 Following publication of the original article [1], we have been notified that affiliation 3 was incorrectly published. It is now: 3 Department of Computer Engineering, Boukan Branch, Islamic Azad University, Tehran, Iran It should be: 3 Department of Computer Engineering, Boukan Branch, Islamic Azad University
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Provably secure data selective sharing scheme with cloud-based decentralized trust management systems J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-10 S. Velmurugan, M. Prakash, S. Neelakandan, Arun Radhakrishnan
The smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged
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Stacked-CNN-BiLSTM-COVID: an effective stacked ensemble deep learning framework for sentiment analysis of Arabic COVID-19 tweets J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-09 Naglaa Abdelhady, Taysir Hassan A. Soliman, Mohammed F. Farghally
Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people’s lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people’s feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we
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Traffic prediction for diverse edge IoT data using graph network J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-08 Tao Shen, Lu Zhang, Renkang Geng, Shuai Li, Bin Sun
More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets
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Predicting UPDRS in Parkinson’s disease using ensembles of self-organizing map and neuro-fuzzy J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-06 Siren Zhao, Jilun Zhang, Jianbin Zhang
Parkinson's Disease (PD) is a complex, degenerative disease that affects nerve cells that are responsible for body movement. Artificial Intelligence (AI) algorithms are widely used to diagnose and track the progression of this disease, which causes symptoms of Parkinson's disease in its early stages, by predicting the results of the Unified Parkinson's Disease Rating Scale (UPDRS). In this study, we
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A cloud-edge computing architecture for monitoring protective equipment J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-06 Carlos Reaño, Jose V. Riera, Verónica Romero, Pedro Morillo, Sergio Casas-Yrurzum
The proper use of protective equipment is very important to avoid fatalities. One sector in which this has a great impact is that of construction sites, where a large number of workers die each year. In this sector as in others, employers are responsible for providing their employees with this equipment. In addition, employers must monitor and ensure its correct use. These tasks are usually performed
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A cloud-edge collaborative task scheduling method based on model segmentation J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-05 Chuanfu Zhang, Jing Chen, Wen Li, Hao Sun, Yudong Geng, Tianxiang Zhang, Mingchao Ji, Tonglin Fu
With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy
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Analysis and prediction of virtual machine boot time on virtualized computing environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-04 Ridlo Sayyidina Auliya, Yen-Lin Lee, Chia-Ching Chen, Deron Liang, Wei-Jen Wang
Starting a virtual machine (VM) is a common operation in cloud computing platforms. In order to achieve better management of resource provisioning, a cloud platform needs to accurately estimate the VM boot time. In this paper, we have conducted several experiments to analyze the factors that could affect VM boot time in a computer cluster with shared storage. We also implemented four models for VM
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IoT workload offloading efficient intelligent transport system in federated ACNN integrated cooperated edge-cloud networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-02 Abdullah Lakhan, Tor-Morten Grønli, Paolo Bellavista, Sajida Memon, Maher Alharby, Orawit Thinnukool
Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles
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Intelligent code search aids edge software development J. Cloud Comp. (IF 3.418) Pub Date : 2024-04-01 Fanlong Zhang, Mengcheng Li, Heng Wu, Tao Wu
The growth of multimedia applications poses new challenges to software facilities in edge computing. Developers must effectively develop edge computing software to accommodate the rapid expansion of multimedia applications. Code search has become a prevalent practice to enhance the efficiency of the construction of edge software infrastructure. Researchers have proposed lots of approaches for code
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Correction to: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non‑stationary data patterns J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-27 Huimin Han, Harold Neira-Molina, Asad Khan, Meie Fang, Haitham A. Mahmoud, Emad Mahrous, Bilal Ahmed, Yazeed Yasin Ghadi
Following publication of the original article [1], we have been notified that one of the authors? names was published incorrectly. Now it is: Harold Neira-Molin 2 It should be: Harold Neira-Molina 2 The original article was updated. Han et al (2024) Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non–stationary data patterns (2024). 13:20 https://doi
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PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-27 Ziwen Zhang, Qi Liu, Xiaodong Liu, Yonghong Zhang, Zihao Du, Xuefei Cao
In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting
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Correction: FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-26 Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li
Correction: Journal of Cloud Computing (2024) 13:57 https://doi.org/10.1186/s13677-024-00623-x Following publication of the original article [1], we have been notified that there is duplicate of the body text in the published article. Now the text is: MLP ((model): Sequential ((0): Linear (in_features=3, out_features=200, bias=True) 1. Dropout (p=0.2, inplace=False) 2. ReLU () 3. Linear (in_features=200
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CG-PBFT: an efficient PBFT algorithm based on credit grouping J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-25 Juan Liu, Xiaohong Deng, Wangchun Li, Kangting Li
Because of its excellent properties of fault tolerance, efficiency and availability, the practical Byzantine fault tolerance (PBFT) algorithm has become the mainstream consensus algorithm in blockchain. However, current PBFT algorithms have problems such as inadequate security of primary node selection, high communication overhead and network delay in the process of consensus. To address these problems
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Time-aware outlier detection in health physique monitoring in edge-aided sport education decision-makings J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-25 Yanjie Li, Liqin Kang, Zhaojin Li, Fugao Jiang, Nan Bi, Tao Du, Maryam Abiri
The increasing popularity of various intelligent sensor and mobile communication technologies has enabled quick health physique sensing, monitoring, collection and analyses of students, which significantly promoted the development of sport education. Through collecting the students’ physiological signals and transmitted them to edge servers, we can precisely analyze and judge whether a student is in
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Accurate and fast congestion feedback in MEC-enabled RDMA datacenters J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-25 Xin He, Feifan Liang, Weibei Fan, Junchang Wang, Lei Han, Fu Xiao, Wanchun Dou
Mobile edge computing (MEC) is a novel computing paradigm that pushes computation and storage resources to the edge of the network. The interconnection of edge servers forms small-scale data centers, enabling MEC to provide low-latency network services for mobile users. Nowadays, Remote Direct Memory Access (RDMA) has been widely deployed in such data centers to reduce CPU overhead and network latency
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Optimus: association-based dynamic system call filtering for container attack surface reduction J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-23 Seungyong Yang, Brent Byunghoon Kang, Jaehyun Nam
While container adoption has witnessed significant growth in facilitating the operation of large-scale applications, this increased attention has also attracted adversaries who exploit numerous vulnerabilities present in contemporary containers. Unfortunately, existing security solutions largely overlooked the need to restrict container access to the shared host kernel, particularly exhibiting critical
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A secure cross-domain authentication scheme based on threshold signature for MEC J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-22 Lei Chen, Chong Guo, Bei Gong, Muhammad Waqas, Lihua Deng, Haowen Qin
The widespread adoption of fifth-generation mobile networks has spurred the rapid advancement of mobile edge computing (MEC). By decentralizing computing and storage resources to the network edge, MEC significantly enhances real-time data access services and enables efficient processing of large-scale dynamic data on resource-limited devices. However, MEC faces considerable security challenges, particularly
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Edge intelligence empowered delivery route planning for handling changes in uncertain supply chain environment J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-22 Gaoxian Peng, Yiping Wen, Wanchun Dou, Tiancai Li, Xiaolong Xu, Qing Ye
Traditional delivery route planning faces challenges in reducing logistics costs and improving customer satisfaction with growing customer demand and complex road traffic, especially in uncertain supply chain environment. To address these challenges, we introduce an innovative two-phase delivery route planning method integrating edge intelligence technology. The novelty of our approach lies in utilizing
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Security issues of news data dissemination in internet environment J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-22 Kang Song, Wenqian Shang, Yong Zhang, Tong Yi, Xuan Wang
With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep
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Short-term forecasting of surface solar incident radiation on edge intelligence based on AttUNet J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-22 Mengmeng Cui, Shizhong Zhao, Jinfeng Yao
Solar energy has emerged as a key industry in the field of renewable energy due to its universality, harmlessness, and sustainability. Accurate prediction of solar radiation is crucial for optimizing the economic benefits of photovoltaic power plants. In this paper, we propose a novel spatiotemporal attention mechanism model based on an encoder-translator-decoder architecture. Our model is built upon
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Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-21 Qing He, Hua Zhao, Yu Feng, Zehao Wang, Zhaofeng Ning, Tingwei Luo
Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented by auction mechanisms and fuzzy optimizers that connect various supply chain stages. Specifically, edge computing offers powerful capabilities
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AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-21 Mughair Aslam Bhatti, Zhiyao Song, Uzair Aslam Bhatti, Syam M. S
The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep
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An integrated SDN framework for early detection of DDoS attacks in cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-20 Asha Varma Songa, Ganesh Reddy Karri
Cloud computing is a rapidly advancing technology with numerous benefits, such as increased availability, scalability, and flexibility. Relocating computing infrastructure to a network simplifies hardware and software resource monitoring in the cloud. Software-Defined Networking (SDN)-based cloud networking improves cloud infrastructure efficiency by dynamically allocating and utilizing network resources
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An optimized neural network with AdaHessian for cryptojacking attack prediction for Securing Crypto Exchange Operations of MEC applications J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Uma Rani, Sunil Kumar, Neeraj Dahiya, Kamna Solanki, Shanu Rakesh Kuttan, Sajid Shah, Momina Shaheen, Faizan Ahmad
Bitcoin exchange security is crucial because of MEC's widespread use. Cryptojacking has compromised MEC app security and bitcoin exchange ecosystem functionality. This paper propose a cutting-edge neural network and AdaHessian optimization technique for cryptojacking prediction and defense. We provide a cutting-edge deep neural network (DNN) cryptojacking attack prediction approach employing pruning
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Privacy-preserving federated learning based on partial low-quality data J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Huiyong Wang, Qi Wang, Yong Ding, Shijie Tang, Yujue Wang
Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to terminal devices. However, the server may still obtain participants’ privacy through inference attacks
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A secure data interaction method based on edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-18 Weiwei Miao, Yuanyi Xia, Rui Zhang, Xinjian Zhao, Qianmu Li, Tao Wang, Shunmei Meng
Deep learning achieves an outstanding success in the edge scene due to the appearance of lightweight neural network. However, a number of works show that these networks are vulnerable for adversarial examples, bringing security risks. The classical adversarial detection methods are used in white-box setting and show weak performances in black-box setting, like the edge scene. Inspired by the experimental
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TCP Stratos for stratosphere based computing platforms J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-15 A. A. Periola
Stratosphere computing platforms (SCPs) benefit from free cooling but face challenges necessitating transmission control protocol (TCP) re-design. The redesign should be considered due to stratospheric gravity waves (SGWs), and sudden stratospheric warming (SSWs). SGWs, and SSWs disturb the wireless channel during SCPs packet communications. SCP packet transmission can be done using existing TCP variants
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Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-15 Zhiqing Bai, Caizhong Li, Javad Pourzamani, Xuan Yang, Dejuan Li
Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features
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SRA-E-ABCO: terminal task offloading for cloud-edge-end environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-14 Shun Jiao, Haiyan Wang, Jian Luo
The rapid development of the Internet technology along with the emergence of intelligent applications has put forward higher requirements for task offloading. In Cloud-Edge-End (CEE) environments, offloading computing tasks of terminal devices to edge and cloud servers can effectively reduce system delay and alleviate network congestion. Designing a reliable task offloading strategy in CEE environments
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FLM-ICR: a federated learning model for classification of internet of vehicle terminals using connection records J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-13 Kai Yang, Jiawei Du, Jingchao Liu, Feng Xu, Ye Tang, Ming Liu, Zhibin Li
With the rapid growth of Internet of Vehicles (IoV) technology, the performance and privacy of IoV terminals (IoVT) have become increasingly important. This paper proposes a federated learning model for IoVT classification using connection records (FLM-ICR) to address privacy concerns and poor computational performance in analyzing users' private data in IoV. FLM-ICR, in the horizontally federated
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Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-08 Yu Hu, Feipeng Qiu, Fei Zheng, Jilong Zhao
In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the
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Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-07 Wen Xing, Adam Slowik, J. Dinesh Peter
With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music recommendation will be a potential development direction for online music education. In this paper, we propose
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RNA-RBP interactions recognition using multi-label learning and feature attention allocation J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-07 Huirui Han, Bandeh Ali Talpur, Wei Liu, Limei Wang, Bilal Ahmed, Nadia Sarhan, Emad Mahrous Awwad
In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures
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Low-cost and high-performance abnormal trajectory detection based on the GRU model with deep spatiotemporal sequence analysis in cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-05 Guohao Tang, Huaying Zhao, Baohua Yu
Trajectory anomalies serve as early indicators of potential issues and frequently provide valuable insights into event occurrence. Existing methods for detecting abnormal trajectories primarily focus on comparing the spatial characteristics of the trajectories. However, they fail to capture the temporal dimension’s pattern and evolution within the trajectory data, thereby inadequately identifying the
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AI-empowered mobile edge computing: inducing balanced federated learning strategy over edge for balanced data and optimized computation cost J. Cloud Comp. (IF 3.418) Pub Date : 2024-03-04 Momina Shaheen, Muhammad S. Farooq, Tariq Umer
In Mobile Edge Computing, the framework of federated learning can enable collaborative learning models across edge nodes, without necessitating the direct exchange of data from edge nodes. It addresses significant challenges encompassing access rights, privacy, security, and the utilization of heterogeneous data sources over mobile edge computing. Edge devices generate and gather data, across the network
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Automated visual quality assessment for virtual and augmented reality based digital twins J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-26 Ben Roullier, Frank McQuade, Ashiq Anjum, Craig Bower, Lu Liu
Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods
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Detection of cotton leaf curl disease’s susceptibility scale level based on deep learning J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-26 Rubaina Nazeer, Sajid Ali, Zhihua Hu, Ghulam Jillani Ansari, Muna Al-Razgan, Emad Mahrous Awwad, Yazeed Yasin Ghadi
Cotton, a crucial cash crop in Pakistan, faces persistent threats from diseases, notably the Cotton Leaf Curl Virus (CLCuV). Detecting these diseases accurately and early is vital for effective management. This paper offers a comprehensive account of the process involved in collecting, preprocessing, and analyzing an extensive dataset of cotton leaf images. The primary aim of this dataset is to support
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Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-23 S. Gayathri, D. Surendran
Anomaly detection in Wireless Sensor Networks (WSNs) is critical for their reliable and secure operation. Optimizing resource efficiency is crucial for reducing energy consumption. Two new algorithms developed for anomaly detection in WSNs—Ensemble Federated Learning (EFL) with Cloud Integration and Online Anomaly Detection with Energy-Efficient Techniques (OAD-EE) with Cloud-based Model Aggregation
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Edge intelligence-assisted animation design with large models: a survey J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-21 Jing Zhu, Chuanjiang Hu, Edris Khezri, Mohd Mustafa Mohd Ghazali
The integration of edge intelligence (EI) in animation design, particularly when dealing with large models, represents a significant advancement in the field of computer graphics and animation. This survey aims to provide a comprehensive overview of the current state and future prospects of EI-assisted animation design, focusing on the challenges and opportunities presented by large model implementations
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Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-19 Meiyan Li, Qinyong Wang, Yuwei Liao
Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation
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Multiple objectives dynamic VM placement for application service availability in cloud networks J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-17 Yanal Alahmad, Anjali Agarwal
Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement
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Investigation on storage level data integrity strategies in cloud computing: classification, security obstructions, challenges and vulnerability J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-15 Paromita Goswami, Neetu Faujdar, Somen Debnath, Ajoy Kumar Khan, Ghanshyam Singh
Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup
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A secure and efficient electronic medical record data sharing scheme based on blockchain and proxy re-encryption J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-15 Guijiang Liu, Haibo Xie, Wenming Wang, Haiping Huang
With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs
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A fog-edge-enabled intrusion detection system for smart grids J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-14 Noshina Tariq, Amjad Alsirhani, Mamoona Humayun, Faeiz Alserhani, Momina Shaheen
The Smart Grid (SG) heavily depends on the Advanced Metering Infrastructure (AMI) technology, which has shown its vulnerability to intrusions. To effectively monitor and raise alarms in response to anomalous activities, the Intrusion Detection System (IDS) plays a crucial role. However, existing intrusion detection models are typically trained on cloud servers, which exposes user data to significant
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Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-14 Mohammad Zunnun Khan, Mohd Shoaib, Mohd Shahid Husain, Khair Ul Nisa, Mohammad. Tabrez Quasim
Cloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to
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Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-13 Junyan Chen, Wei Xiao, Hongmei Zhang, Jiacheng Zuo, Xinmei Li
Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve
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Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-12 Jing Chen, Shengyi Yang, Ting Gao, Yue Ying, Tian Li, Peng Li
The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining
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Evaluation of AI tools for healthcare networks at the cloud-edge interaction to diagnose autism in educational environments J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-09 Yue Pan, Andia Foroughi
Physical, social, and routine environments can be challenging for learners with autism spectrum disorder (ASD). ASD is a developmental disorder caused by neurological problems. In schools and educational environments, this disorder may not only hinder a child’s learning, but also lead to more crises and mental convulsions. In order to teach students with ASD, it is essential to understand the impact
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Predicting the individual effects of team competition on college students’ academic performance in mobile edge computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-09 Huiling Zhang, Huatao Wu, Zhengde Li, Wenwen Gong, Yan Yan
Mobile edge computing (MEC) has revolutionized the way of teaching in universities. It enables more interactive and immersive experiences in the classroom, enhancing student engagement and learning outcomes. As an incentive mechanism based on social identity and contest theories, team competition has been adopted and shown its effectiveness in improving students’ participation and motivation in college
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Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-08 Mamoona Humayun, Amjad Alsirhani, Faeiz Alserhani, Momina Shaheen, Ghadah Alwakid
Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET
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Harmfulness metrics in digital twins of social network rumors detection in cloud computing environment J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-08 Hao Li, Wu Yang, Wei Wang, Huanran Wang
Social network rumor harm metric is a task to score the harm caused by a rumor by analyzing the spreading range of the rumor, the users affected, the repercussions caused, etc., and then the harm caused by the rumor. Rumor hazard metric models can help rumor detection digital twins to understand and analyze user behaviors and assist social network network managers to make more informed decisions. However
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Multiobjective trajectory optimization algorithms for solving multi-UAV-assisted mobile edge computing problem J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-07 Mohamed Abdel-Basset, Reda Mohamed, Ibrahim M. Hezam, Karam M. Sallam, Abdelaziz Foul, Ibrahim A. Hameed
The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network
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Challenges in remote sensing based climate and crop monitoring: navigating the complexities using AI J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-06 Huimin Han, Zehua Liu, Jiuhao Li, Zhixiong Zeng
The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO2 concentrations. It also interacts with other ecological stresses like
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Graph convolution networks for social media trolls detection use deep feature extraction J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-06 Muhammad Asif, Muna Al-Razgan, Yasser A. Ali, Long Yunrong
This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate
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DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing J. Cloud Comp. (IF 3.418) Pub Date : 2024-02-05 Qi Liu, Jiawei Sun, Yonghong Zhang, Xiaodong Liu
In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric refraction, equipment malfunctions, and other factors, resulting in diminished data quality. Traditional radar blockage correction methods, such as employing