Performance optimization of IoT based biological systems using deep learning

Irshad, Omer; Khan, Muhammad Usman Ghani; Basheer, Shakila; Bashir, Ali Kashif

doi:10.1016/j.comcom.2020.02.059

Cited by 17 publications

(9 citation statements)

References 39 publications

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“…Smart grids also use renewable energy resources to be safely plugged into the grid to appendage the power supply. The intelligent systems, such as Expert Systems (ESs), Fuzzy Logic (FL) [6], [7], Machine Learning (ML) [8], [9] and Deep Neural Networks (DNN) [10]- [12] have revolutionized the power distribution process. These systems render effective tools for design, simulation, fault diagnostics, and fault-tolerant control in the modern smart grid [13].…”

Section: Introductionmentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches. INDEX TERMS Multidirectional long short-term memory (MLSTM), machine learning (ML), smart grid (SG), cyber physical systems (CPS).

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Section: Introductionmentioning

confidence: 99%

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

et al. 2020

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“…Stellar mass black hole optimization was used for utility mining in [17]. There were also numerous recent published papers dealing with different applications of the machine learning and deep learning approaches, ranging from the sentiment analysis [18], flood prediction [19], the Dengue disease prediction [20], other medical diagnostics [21][22][23], all the way to eHealth and IoT [24][25][26].…”

Section: Background and Related Workmentioning

confidence: 99%

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Bačanin¹,

Alhazmi²,

Živković³

et al. 2022

Computers, Materials &Amp; Continua

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In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets.

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“…Thus, delay tolerant data was achieved in this system. Irshad et al [14] has proposed a framework for the optimization of performance of biological systems using IoT network. Here, deep learning was utilized for achieving optimization.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

Nguyen¹,

Akila²,

Pal³

et al. 2022

Computers, Materials &Amp; Continua

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This article presents a new scheme for dynamic data optimization in IoT (Internet of Things)-assisted sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio, coverage ratio and sensing error. Data optimization was performed based on the availability of cloud resource, sensor energy, data flow volume and the centroid of each state. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.

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Performance optimization of IoT based biological systems using deep learning

Cited by 17 publications

References 39 publications

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform

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