Study of biological networks using graph theory

Gao, Wei; Wu, H.; Siddiqui, Muhammad Kamran; Baig, Abdul Qudair

doi:10.1016/j.sjbs.2017.11.022

Cited by 243 publications

(94 citation statements)

References 40 publications

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“…An artificial neural network (ANN) mimics the human neural system [32] to make an intelligent processor capable of being used for many regression and classification aims with complicated non-linear conditions [33]. The high prediction capability of the ANN has made its different notions popular for various engineering simulations [34][35][36][37][38]. Multi-layer perceptron (MLP) is known as the most common type of ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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“…Mathematical tools help to solve many problems arising in chemistry and other areas of sciences [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60], for example, graph theory help us to know about structural and physico-chemcial properties of chemcial compounds without using wet labs [50][51][52][53][54]. The finite groups are helpful in studying symmetry of molcules because almost all organic and inorganic compounds are symmetric about its center [56][57][58][59][60].…”

Section: Discussionmentioning

confidence: 99%

Generators of Maximal Subgroups of Harada-Norton and some Linear Groups

et al. 2019

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Group theory, the ultimate theory for symmetry, is a powerful tool that has a direct impact on research in robotics, computer vision, computer graphics and medical image analysis. Symmetry is very important in chemistry research and group theory is the tool that is used to determine symmetry. Usually, it is not only the symmetry of molecule but also the symmetries of some local atoms, molecular orbitals, rotations and vibrations of bonds, etc. that are important. Harada-Norton group is an example of a sporadic simple group. There are 14 maximal subgroups of Harada-Norton group. Generators (also known as words) of 11 maximal subgroups are already known. The aim of this note is to give generators of the remaining 3 maximal subgroups, which is an open problem mentioned on A World-wide-web Atlas of Group Representations (http://brauer.maths.qmul.ac.uk/Atlas) [1]. In this report we compute the generators of A 6 × A 6 .D 8 , 2 3+2+6 .(3 × L 3 (2)) and 3 4 : 2.(A 4 × A 4 ).4. Moreover we also compute the generators for the Maximal subgroups of some linear groups.

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“…Thus, implementing direct evaluative methods for exploring the relationship between the shear strength and its related parameters is relatively costly and complicated [15,16], and also there might lead to different results because of different stress states, stress histories, degrees of sampling disturbance, and strain rates [17]. Due to these difficulties, indirect methods are very popular in the field of geotechnical parameter evaluation [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020

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The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

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Study of biological networks using graph theory

Cited by 243 publications

References 40 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Generators of Maximal Subgroups of Harada-Norton and some Linear Groups

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

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