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Optimizing the neural network hyperparameters utilizing genetic algorithm

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2021年第6期22卷 407-426页

作者： Saeid NIKBAKHT Cosmin ANITESCU Timon RABCZUK Division of Computational Mechanics Ton Due Thang UniversityHo Chi Minh CityVietnam Faculty of Civil Engineering Ton Due Thang UniversityHo Chi Minh CityVietnam Institute of Structural Mechanics Bauhaus-Universitdt WeimarWeimar 99423Germany

Neural networks(NNs),as one of the most robust and efficient machine learning methods,have been commonly used in solving several ***,choosing proper hyperparameters(*** numbers of layers and neurons in each layer)has a significant influence on the accuracy of these ***,a considerable number of studies have been carried out to optimize the NN *** this study,the genetic algorithm is applied to NN to find the optimal ***,the deep energy method,which contains a deep neural network,is applied first on a Timoshenko beam and a plate with a ***,the numbers of hidden layers,integration points,and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these ***,applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.

关键词： Machine learning Neural network(NN) hyperparameters Genetic algorithm

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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Computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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An Improved Convolutional Neural Network Model for DNA Classification

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Computers, Materials & Continua 2022年第3期70卷 5907-5927页

作者： Naglaa.F.Soliman Samia M.Abd-Alhalem Walid El-Shafai Salah Eldin S.E.Abdulrahman N.Ismaiel El-Sayed M.El-Rabaie Abeer D.Algarni Fathi E.Abd El-Samie Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt

Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)*** any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature *** downsampling is an improved form of the existing pooling layer to obtain better classification *** two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for *** convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature ***,there are parameters which directly affect how a CNN model is *** this paper,some issues concerned with the training of CNNs have been *** CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of *** and assessment of the performance of CNNs are carried out on 16S rRNA bacterial *** results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.

关键词： DNA classification CNN downsampling hyperparameters DL 2D DT 2D RP

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Comparison of Deep Learning Architectures for Late Blight and Early Blight Disease Detection on Potatoes

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Open Journal of Applied Sciences 2022年第5期12卷 723-743页

作者： Soumo Emmanuel Arnaud Ndeda Rehema Shohei Aoki Murungi Lucy Kananu Department of Mechatronics Engineering Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Horticulture Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Potato late blight and early blight are common hazards to the long-term production of potatoes, impacting many farmers around the world, particularly in Africa. Early detection and treatment of the potato blight disease are critical for promoting healthy potato plant growth and ensuring adequate supply and food security for the fast-growing population. As a result, machine-driven disease detection systems may be able to overcome the constraints of traditional leaf disease diagnosis procedures, which are generally time-consuming, inaccurate, and costly. Convolutional Neural Networks (CNNs) have been shown to be effective in a variety of agricultural applications. CNNs have been shown to be helpful in detecting disease in plants because of their capacity to analyze vast volumes of data quickly and reliably. However, the method hasn’t been widely used in the detection of potato late blight and early blight diseases, which reduce yields significantly. The goal of this study was to compare six cutting-edge CNN architectural models, taking into account transfer learning for training and four hyperparameters. The CNN architectures evaluated were AlexNet, GoogleNet, SqueezeNet, DenseNet121, EfficientNet b7, and VGG19. Likewise, the hyperparameters analyzed were the number of epochs, the batch size, the optimizer, and the learning rate. An open-source dataset containing 4082 images was used. The DenseNet121 architecture with a batch of 32 and a Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01 produced the best performance, with an accuracy of 98.34% and a 97.37% f1-score. The DenseNet121 model was shown to be useful in developing computer vision systems that aid farmers in improving their disease management systems for potato cultivation.

关键词： Image Classification Convolutional Neural Networks Transfer Learning hyperparameters

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