A New Multi-layer Perceptrons Trainer Based on Ant Lion Optimization Algorithm

Yamany, Waleed; Tharwat, Alaa; Hassanin, Mohammad F.; Gaber, Tarek; Hassanien, Aboul Ella; Kim, Tai-hoon

doi:10.1109/isi.2015.9

Cited by 56 publications

(23 citation statements)

References 13 publications

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“…In this section, four experiments were conducted. The first experiment (in Section 4.1 ) has four goals (1) Testing the proposed NRCS model for predicting the toxicity of the biotransformed drugs, (2) Testing the power of this system to deal with uncertain data without using any feature selection or any pre-processing method, (3) Comparing NRCS with conventional classifiers such as Multi-Layer Perceptron (MLP) ( Yamany et al, 2015 ), k-Nearest Neighbors ( k -NN) ( Tharwat, Mahdi, Elhoseny, & Hassanien, 2018 ), and Linear Discriminant Analysis (LDA) ( Tharwat, 2016 ) classifiers, and finally, (4) Comparing the proposed models (NRCS and GNRCS). Different runs were conducted for finding the optimal or near optimal parameters for k -NN and MLP classifiers.…”

Section: Resultsmentioning

confidence: 99%

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Basha

Tharwat

Abdalla

et al. 2019

Expert Systems with Applications

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a b s t r a c tMeasuring toxicity is an important step in drug development. However, the current experimental methods which are used to estimate the drug toxicity are expensive and need high computational efforts. Therefore, these methods are not suitable for large-scale evaluation of drug toxicity. As a consequence, there is a high demand to implement computational models that can predict drug toxicity risks. In this paper, we used a dataset that consists of 553 drugs that biotransformed in the liver. In this data, there are four toxic effects, namely, mutagenic, tumorigenic, irritant and reproductive effects. Each drug is represented by 31 chemical descriptors. This paper proposes two models for predicting drug toxicity risks. The proposed models consist of three phases. In the first phase, the most discriminative features are selected using rough set-based methods to reduce the classification time and improve the classification performance. In the second phase, three different sam pling algorithms, namely, Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique (SMOTE) are used to obtain balanced data. In the third phase, the first proposed model employs the Neutrosophic Rule-based Classification System (NRCS), and the second model uses Genetic NRCS (GNRCS) to classify an unknown drug into toxic or non-toxic. The experimental results proved that the proposed models obtained high sensitivity (89-93%), specificity (91-97%), and GM (90-94%) for all toxic effects. Overall, the results of the proposed models indicate that it could be utilized for the prediction of drug toxicity in the early stages of drug development.

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Section: Resultsmentioning

confidence: 99%

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Basha

Tharwat

Abdalla

et al. 2019

Expert Systems with Applications

Self Cite

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“…To determine the weights and biases of the Multi-layer Perceptrons (MLP), Yamany et al [171] used ALO to train MLP trying to obtain the highest classification rate and the lowest error value. Heidari et al in [172] introduced a hybrid technique called ALOMLP which is based on a hybrid training methods ALO and Multi-Layer-Perceptrons (MLPs).…”

Section: ) Neural Networkmentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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“…The neuron's input is the sum of weights on its inputs. The equation 10 (11) At last output is classified according to calculated nodes of hidden output, which is illustrated as equation (12) and (13), [38]. Here, the ALO algorithm is used as a trainer to parameters of ANN.…”

Section: Training Process Of Ann With Alo Step 1: Read In Input and Omentioning

confidence: 99%

An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique

George

Ganesan

2020

IJEAT

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Nowadays, the PID controller is very common controller as well as very important controller in industrial utilizations. In the paper, proposed an ALO algorithm and ANN controller is utilized to enhance PID controller performance and control the tuning of TDS. TDS stands for Time delay system. ALO stands for Ant lion optimizer and ANN stands for Artificial neural network. In terms of parameters controlling, the time delay system is controlled and for different delay events low overshoot and fast time settling is reached. The novelty of the presented method is enhancing the PID controller performance by optimizing the PID gain parameters and controlling the highorder TDS. The performance of time delay system can be enhanced through decreasing error, tracking, time delay & error, rapid and exactly for their corresponding reference values. For parameter controlling of time delay system along optimal values, can be significantly enhanced the performance. To analyze the characteristics of the presented method, the various time delay systems are analyzed. The input and gain parameters were utilized to evaluate the objective function from tuning system. Based on proposed method, the optimal result is achieved and evaluated the increae time, settling time, overshoot as well as steady state error in TDS. The suggested controller is executed in MATLAB/Simulink work site and the presented technique performance examined through performance indexes and time domain specifications are evaluated using presented method compared to previous methods like ABC (Artificial Bee colony) algorithm, GSA (Gravitational Search Algorithm) ,FA (Firefly Algorithm).

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A New Multi-layer Perceptrons Trainer Based on Ant Lion Optimization Algorithm

Cited by 56 publications

References 13 publications

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Neutrosophic rule-based prediction system for toxicity effects assessment of biotransformed hepatic drugs

Ant Lion Optimization: Variants, Hybrids, and Applications

An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique

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