Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding

Berahmand, Kamal; Nasiri, Elahe; Mohammadiani, Rojiar Pir; Li, Yuefeng

doi:10.1016/j.compbiomed.2021.104933

Cited by 87 publications

(28 citation statements)

References 40 publications

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“…The fog infrastructure can be considered a graph where cells are devices and edges are direct network connections between devices 5 . Fog cells and IoT devices work with another group of IoT devices to provide virtual resources.…”

Section: Introductionmentioning

confidence: 99%

“…4 The fog infrastructure can be considered a graph where cells are devices and edges are direct network connections between devices. 5 Fog cells and IoT devices work with another group of IoT devices to provide virtual resources. These cells enable IoT services in the vicinity of data sources instead of the cloud, which leads to lower communication latencies and better utilization of previous storage and network resources in the fog.…”

mentioning

confidence: 99%

“…Therefore, if the service a k,s is located on the fog cell F i,j , the element P F i,j a k,s will be placed 1, otherwise equal to 0. The objective function of the stated problem is to maximize the number of service placement in the fog computing cells in such a way that it satisfies the application deadline, as shown in Equation (5).…”

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confidence: 99%

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Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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Internet of Things (IoT) represents a new generation of information and communication technology for anyone, anytime and anywhere. Cloud service‐based IoT applications significantly increase latency and network utilization. The fog environment is closer to the user to perform computing, communication, and storage tasks on network edge devices. Therefore, it can greatly reduce the latency of real‐time applications. It is an essential feature of fog computing and its most important advantage compared to cloud computing. This study proposed a new approach to service placement generated by running applications on IoT devices in the fog computing. IoT devices send applications to the fog environment that each application contains a set of services. The purpose of solving the IoT services placement problem is to efficiently deploy these services on fog cells. For this purpose, it is assumed that the received services from the IoT applications are received as a directed acyclic graph that depicts the communication between the cells within the graph that shows the communication between the services. Then, the imperialist competitive algorithm is used to place and select the destination for IoT services. The simulation results of the iFogSim simulator in different experiments showed that the imperialist competitive algorithm with the proposed graph partitioning approach has improved service placement on the fog infrastructure compared to the genetic algorithm and best‐fit algorithm.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

2022

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“…Cloud computing provides a platform for servers in cloud data centers (CDCs) to share resources with users when they request them. Service providers can rent VMs from cloud providers to provide various services to their users 5 . Because different service providers rent the required VMs from cloud providers, the main challenge is to provide efficient techniques for deploying VMs on PSs, where in addition to maximizing the utilization of PSs, the quality of service (QoS) requirements required to satisfy 3 …”

Section: Introductionmentioning

confidence: 99%

“…Service providers can rent VMs from cloud providers to provide various services to their users. 5 Because different service providers rent the required VMs from cloud providers, the main challenge is to provide efficient techniques for deploying VMs on PSs, where in addition to maximizing the utilization of PSs, the quality of service (QoS) requirements required to satisfy. 3 Cloud service providers are collections of several CDCs around the world to receive user requests over the Internet and service them in optimal methods.…”

Section: Introductionmentioning

confidence: 99%

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

Wang¹,

Osmani

Mirzaei

2022

Trans Emerging Tel Tech

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Recently, cloud computing is growing rapidly and contributing to the realization of other technologies. Cloud data centers use a lot of energy to host applications, which leads to high operating costs and increased carbon dioxide emissions into the environment. Dynamic consolidation of virtual machines (VMs) into the minimum number of physical servers is an efficient approach to managing energy consumption in cloud. Virtual machines placement (VMP) is an important problem in the proper consolidation of VMs and is the most common way to improve resource utilization. In VMP problem, VMs are consolidated to minimize the number of active physical servers. This consolidation is done using migration of VMs. After placement, the VMs execute on selected physical servers and the underloaded physical servers are off to optimize energy consumption. Basically, the purpose of VMP is to allocate a set of VMs to a subset of physical servers, taking into account some objectives such as reducing energy consumption and violating the service level agreement (SLA). In this article, an improved teaching-learning based optimization (TLBO) algorithm is formulated to solve VMP as a multi-objective problem, which we call VMP-TLBO. VMP-TLBO can perform optimal placement of migrated VMs to physical servers with the purpose of reducing energy consumption and SLA violation. VMP-TLBO is implemented in MATLAB and the experimental results show that proposed algorithm does not violate SLA and compared to the best equivalence algorithm (ie, PAPSO), it improves energy consumption and SLA violation by 1.8% and 5.6%, respectively. In addition, VMP-TLBO consolidates migrated VMs into the minimum number of physical servers to minimize the number of overloaded hosts as much as possible.

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A novel two‐phase near‐infrared and midinfrared wavelength selection framework for sample classification

Fontes,

Anzanello,

Brito

et al. 2024

Journal of Chemometrics

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Spectral data describing product samples are typically composed of a large number of noisy and irrelevant wavelengths that tends to undermine the performance of multivariate predictive techniques. This paper proposes a two‐phase framework that integrates a preselection wavelength step oriented by wavelength clustering to a wrapper‐based strategy. The first phase performs a pruning process in the data that removes the less informative wavelengths relying on the spectral clustering, a technique deemed suitable to the Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy data at hand. The preselected wavelengths undergo a second phase of selection efforts based on the combination of different wavelength importance indices (i.e., Bhattacharyya distance, Chi‐square, ReliefF, and Gini) and classification techniques (i.e., support vector machine, k‐nearest neighbors, and random forest). When applied to 11 FTIR datasets from different domains, the recommended combination of importance index and classifier increased the average accuracy by 6.37% (from 0.863 to 0.918), while retaining average 3.84% of the original spectra. The framework also improved the selection process regarding computational time.

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Spectral clustering on protein-protein interaction networks via constructing affinity matrix using attributed graph embedding

Cited by 87 publications

References 40 publications

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

Deploying IoT services on the fog infrastructure: A graph partitioning‐based approach

Dynamic placement of virtual machines using an improved multi‐objective teaching‐learning based optimization algorithm in cloud

A novel two‐phase near‐infrared and midinfrared wavelength selection framework for sample classification

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