scholarly journals Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Saad Mohamed Darwish
Keyword(s):  
Neural Network ◽  
Genetic Programming ◽  
Goodness Of Fit ◽  
Programming Model ◽  
Computational Cost ◽  
Chemical Compounds ◽  
Vital Role ◽  
Internal Validation ◽  
Fit Statistics ◽  
The Neural Network

Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree more accurately. Quantum computing is employed to improve the selection of the best-of-run individuals and handles parsimony pressure to reduce the complexity of the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.

scholarly journals Optimization of machining parameters during Drilling by Taguchi based Design of Experiments and Validation by Neural Network

2018 ◽  
Vol 15 (2) ◽  
pp. 294-301
Author(s):  
Reddy Sreenivasulu ◽  
Chalamalasetti SrinivasaRao
Keyword(s):  
Neural Network ◽  
Design Of Experiments ◽  
Computational Cost ◽  
Machining Parameters ◽  
Burr Size ◽  
Taguchi Design Of Experiments ◽  
The Neural Network ◽  
Input Parameters ◽  
Hole Making ◽  
The Cost

Drilling is a hole making process on machine components at the time of assembly work, which are identify everywhere. In precise applications, quality and accuracy play a wide role. Nowadays’ industries suffer due to the cost incurred during deburring, especially in precise assemblies such as aerospace/aircraft body structures, marine works and automobile industries. Burrs produced during drilling causes dimensional errors, jamming of parts and misalignment. Therefore, deburring operation after drilling is often required. Now, reducing burr size is a serious topic. In this study experiments are conducted by choosing various input parameters selected from previous researchers. The effect of alteration of drill geometry on thrust force and burr size of drilled hole was investigated by the Taguchi design of experiments and found an optimum combination of the most significant input parameters from ANOVA to get optimum reduction in terms of burr size by design expert software. Drill thrust influences more on burr size. The clearance angle of the drill bit causes variation in thrust. The burr height is observed in this study.  These output results are compared with the neural network software @easy NN plus. Finally, it is concluded that by increasing the number of nodes the computational cost increases and the error in nueral network decreases. Good agreement was shown between the predictive model results and the experimental responses.  

scholarly journals Practical Aspects of the Design and Use of the Artificial Neural Networks in Materials Engineering

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1832
Author(s):  
Wojciech Sitek ◽  
Jacek Trzaska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Vital Role ◽  
Materials Engineering ◽  
The Neural Network ◽  
Critical Issues ◽  
Artificial Neural ◽  
The Individual

Artificial neural networks are an effective and frequently used modelling method in regression and classification tasks in the area of steels and metal alloys. New publications show examples of the use of artificial neural networks in this area, which appear regularly. The paper presents an overview of these publications. Attention was paid to critical issues related to the design of artificial neural networks. There have been presented our suggestions regarding the individual stages of creating and evaluating neural models. Among other things, attention was paid to the vital role of the dataset, which is used to train and test the neural network and its relationship to the artificial neural network topology. Examples of approaches to designing neural networks by other researchers in this area are presented.

scholarly journals Scalable Evolutionary Computation for Efficient Information Extraction from Remote Sensed Imagery

2014 ◽  
Vol XL-1 ◽  
pp. 27-34
Author(s):  
L. M. Almutairi ◽  
S. Shetty ◽  
H. G. Momm
Keyword(s):  
Cloud Computing ◽  
Genetic Programming ◽  
Information Extraction ◽  
Evolutionary Computation ◽  
Programming Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Extraction Process ◽  
Computing Environment ◽  
Cloud Computing Environment

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.

scholarly journals Predicting the Thermal Performance for the Multi-Objective Vehicle Underhood Packing Optimization Problem

2012 ◽  
Author(s):  
Ravi Teja Katragadda ◽  
Sreekanth Reddy Gondipalle ◽  
Paolo Guarneri ◽  
Georges Fadel
Keyword(s):  
Neural Network ◽  
Thermal Performance ◽  
Thermal Analyses ◽  
Vital Role ◽  
Multi Objective Optimization ◽  
Vehicle Performance ◽  
Multi Objective ◽  
Design Changes ◽  
The Neural Network ◽  
Optimization Routine

The ever increasing demands towards improvement in vehicle performance and passenger comfort have led the automotive manufacturers to further enhance the design in the early stages of the vehicle development process. Though, these design changes enhance the overall vehicle performance to an extent, the placement of these components under the car hood also plays a vital role in increasing the vehicle performance. In the past, a study on the automobile underhood packaging or layout problem was conducted and a multi-objective optimization routine with three objectives namely, minimizing center of gravity height, maximizing vehicle components accessibility and maximizing survivability (for army vehicles) has been setup to determine the optimal locations of the underhood components. The previous study did not consider thermal performance as an objective. This study asserts the necessity of including thermal performance as an objective and makes an assessment of the several available thermal analyses that are performed on the automotive underhood to evaluate the thermal objective. A Neural Network approximation of the CFD analysis conducted over the automotive underhood is presented in this paper. The results obtained from the Neural Network are compared with the CFD results, showing good agreement. The Neural Network model is included in the multi-objective optimization routine and new layout results are obtained. A non-deterministic evolutionary multi-objective algorithm (AMGA-2) is used to perform the optimization process.

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations

2015 ◽  
Vol 23 (2) ◽  
pp. 249-277 ◽  
Author(s):  
Jürgen Branke ◽  
Torsten Hildebrandt ◽  
Bernd Scholz-Reiter
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithms ◽  
Genetic Programming ◽  
Manufacturing Systems ◽  
Job Shop ◽  
Dispatching Rules ◽  
Tree Representation ◽  
Linear Representations ◽  
The Neural Network ◽  
Dynamic Stochastic

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

scholarly journals Research on Network Layer Recursive Reduction Model Compression for Image Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongfei Ling ◽  
Weiwei Zhang ◽  
Yingjie Tao ◽  
Mi Zhou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Computational Cost ◽  
Network Size ◽  
Compression Technique ◽  
Network Layer ◽  
Network Layers ◽  
Model Compression ◽  
The Neural Network

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.

scholarly journals Survey on Heart Disease Anticipation Through Genetic Algorithm and BP-Neural Network

2021 ◽  
pp. 455-460
Author(s):  
Jaishri ◽  
Santosh Biradar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Medical Diagnosis ◽  
Vital Role ◽  
Classification Problems ◽  
The Neural Network ◽  
Medical Diagnosis System ◽  
Good Set

Medical Diagnosis Systems play a vital role in medical practice and are used by medical practitioners for diagnosis and treatment. In this paper, a medical diagnosis system is presented for predicting the risk of cardiovascular disease. This system is built by combining the relative advantages of genetic algorithm and neural network. Multilayered feed forward neural networks are particularly suited to complex classification problems. The weights of the neural network are determined using genetic algorithm because it finds acceptably good set of weights in less number of iterations. The dataset provided by University of California, Irvine (UCI) machine learning repository is used for training and testing. It consists of 303 instances of heart disease data each having 14 attributes including the class label. First, the dataset is preprocessed in order to make them suitable for training. Genetic based neural network is used for training the system. The final weights of the neural network are stored in the weight base and are used for predicting the risk of cardiovascular disease. The classification accuracy obtained using this approach is 94.17%.

scholarly journals A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS

2018 ◽  
Vol 26 (2) ◽  
pp. 88-97 ◽  
Author(s):  
Ali Mansourkhaki ◽  
Mohammadjavad Berangi ◽  
Majid Haghiri ◽  
Mohammadreza Haghani
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Goodness Of Fit ◽  
Measurement Data ◽  
Reflection Factor ◽  
Road Traffic Noise ◽  
The Neural Network ◽  
Marquardt Algorithm

Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.

scholarly journals Enhancement of damaged-image prediction through Cahn–Hilliard image inpainting

2021 ◽  
Vol 8 (5) ◽  
pp. 201294
Author(s):  
José A. Carrillo ◽  
Serafim Kalliadasis ◽  
Fuyue Liang ◽  
Sergio P. Perez
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Image Inpainting ◽  
Finite Volume Scheme ◽  
Binary Images ◽  
Hilliard Equation ◽  
The Neural Network ◽  
Image Prediction ◽  
Different Types ◽  
Cahn Hilliard Equation

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn–Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn–Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn–Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.

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