Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks

Heydarpour, F.; Abbasi, Elham; Ebadi, M. J.; Karbassi, S. M.

doi:10.9781/ijimai.2020.11.011

Cited by 26 publications

(19 citation statements)

References 32 publications

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“…The following clues to be able to lead to good research work include the following: investigation of threephase flows with the method discussed in this paper; various methods such as Bayesian networks, decision trees and even the use of deep neural networks can be good suggestions for researchers in this field to study in the future. The methods used in this research can also be applied in various fields of science, including medicine [33,34]. [9] Frequency…”

Section: Resultsmentioning

confidence: 99%

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

et al. 2022

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In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

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Section: Resultsmentioning

confidence: 99%

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

et al. 2022

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“…Although it is growing rapidly, it is still largely unexplored. AI has many fields, the most popular fields in the recent period are neural networks [3][4][5][6][7], and data science which includes the Team formation problem as one of the most popular problems in data science.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

Nageh¹,

Elshamy²,

Hassan³

et al. 2022

Computers, Materials &Amp; Continua

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Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO's optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.

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“…Mobile nodes can adopt any route (either arbitrary or fixed path) for message transfer. OppNets are also used in advanced applications [22][23][24] like; data science, machine learning, and etc.…”

Section: Types Of Oppnets Routingmentioning

confidence: 99%

Persuasive Simulation of Optimized Protocol for OppNet

Dalal¹,

Khari²

2021

DSA

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The new paradigm approach to provide communication in heterogeneous network with distinct communication technologies is known as Opportunistic Network (OppNets). This network permits the mobile nodes to cooperate in diversified social communication like micro-blogs and content dissemination. Due to discontinuous connections, lower bandwidth, and arbitrary nature of OppNets, establishing a new protocol for OppNets routing is a primary challenge. This paper introduces peculiar protocol for OppNets, it integrates the terrific features of random forest decision tree algorithm and firefly nature inspired algorithm. This protocol is trained by few features of Direct delivery protocol, after this machine learning algorithm Random Forest (RF) is used to find the reliable relay nodes (more than one) for transmitting the packet from source to destination node. After RF phase, only one best appropriate node is selected based on firefly optimization algorithm for transferring the packet. This protocol performs better than other protocol like first contact, epidemic, and direct delivery; in terms of message delivery probability, average hop count, overhead ratio, and average latency. Proposed protocol generates 93% lesser average hop-count, 88% reduced overhead ratio, and 66% higher delivery probability as compared to first contact protocol.

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Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks

Cited by 26 publications

References 32 publications

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

Persuasive Simulation of Optimized Protocol for OppNet

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