Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Syulistyo, Arie Rachmad; Purnomo, Dwi; Rachmadi, Muhammad Febrian; Wibowo, Adi

doi:10.21609/jiki.v9i1.366

Cited by 43 publications

(14 citation statements)

References 13 publications

(15 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weight is divided by the convolution layer while the sampling function is carried out by the pooling layer for the output generated by the convolution layer and the data rate is reduced from the layer below it. Output for several fully connected layers uses the output from the pooling layer [9].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

Syahrir¹,

Wibowo²

2021

itsdi

View full text Add to dashboard Cite

One part of the tree, namely the leaves, which grow on the branches, has several types of leaves consisting of 4 shapes, ranging from circular shapes, elongated shapes, and some even have a finger shape. Often we mistake the shapes of these leaves. This study discusses the classification of leaves based the shape of the leaf bones using the Convolutional Neural Network, which is used to classify data that has been labeled using one of the methods, namely supervised learning. The purpose of this method is to classify a variable into the variables that have been listed. The goal is to classify leaves based on leaf shape to implement a Convolutional Neural Network algorithm model for leaf classification based on bone shape, which will produce an accuracy value. Accuracy values are obtained from conducting experiments at the training and trial stages. So it can be concluded using the epochs parameter of 30 and a batch size of 128, using ReLU and Softmax activations. The results obtained for the accuracy value for training are 98.52%, while the validation is 89.06%.

show abstract

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

Syahrir¹,

Wibowo²

2021

itsdi

View full text Add to dashboard Cite

show abstract

“…So the particle change their positions and it gets updated to the nearest one which is considered as its best position. So the evolving process helps it to find the optimum one [76].…”

Section: B Grey Wolf Optimizationmentioning

confidence: 99%

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

et al. 2021

View full text Add to dashboard Cite

Artificial intelligence is a spectacular part of computer engineering that has earned a compelling diversion in the field of medical data classification due to its state-of-art algorithmic strength and learning capabilities. Machine Learning is a major sub-domain of artificial intelligence, where it has become one of most promising fields in computer science. In recent years, there is a large spectrum of healthcare and biomedical data has been growing intensely. Due to the huge labeled or unlabeled data, it is important to have a compact and robust machine learning solution for classification. Several optimizers have been deployed to improve the inclusive performance of machine learning models. The classification of machine learning models depend on several factors. This comprehensive review paper aims to insight the current stage of optimized machine learning success on medical data classification. Increasing number of unstructured medical data has been utilizing in machine learning algorithms to predict intuitions. But it is difficult to inherent immense intuition from those data. So machine learning researchers have utilized stateof-art optimizers and novel feature selection techniques to overcome and emend the performance accuracy. We have highlighted some recent literatures, which exhibits the robust impact of optimizers and feature selection on machine learning techniques on medical data characterization. On the other hand, a cleancut introduction on machine learning and theoretical outlook of widely utilized optimization techniques like genetic algorithm, gray wolf optimization, and particle swarm optimization are discussed for initial understanding to the optimization techniques.

show abstract

“…The hyper-parameters in the Deep-Res-Bidir-LSTM network affect the final result. Generally used methods of tuning parameters include experimental methods, grid searches [26], genetic algorithm (GA) [27], and particle swarm optimization (PSO) [28] [29]. As experimental methods involve approximating the value by running many experiments, these methods are time consuming.…”

Section: Hyper-parameters Settingmentioning

confidence: 99%

Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

Zhao

Yang

Chevalier

et al. 2018

Mathematical Problems in Engineering

236

127

View full text Add to dashboard Cite

Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) cells is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as highways for gradients, which can pass underlying information directly to the upper layer, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked deeply) dimensions, aiming to enhance the recognition rate.When tested with the Opportunity data set and the public domain UCI data set, the accuracy was increased by 4.78% and 3.68%, respectively, compared with previously reported results. Finally, the confusion matrix of the public domain UCI data set was analyzed. IntroductionIn real life, many problems can be described as time series problems. Indeed, human activity recognition (HAR) is of value in both theoretical research and actual practice. It can be used widely, including in health monitoring [1][2], smart homes [3][4], and human-computer interactions [5][6]; for example, LSTM cells are a good choice for solving HAR problems. Unlike traditional algorithms, LSTM can catch relationships in data on the temporal dimension without having to mix the time steps together as a 1D convolutional neural network (CNN) would do. As more of what is commonly called "big data" emerges, LSTM architecture can offer great performance and many potential applications. More specifically, HAR is the process of obtaining action data with sensors; it symbolizes the action information and then allows understanding and extraction of the motion characteristics, which is what activity recognition refers to. Because of the spatial complexity and temporal divergence of behavior, there is no unified recognition method. A public domain benchmark of HAR has been introduced, and different methods of recognition have been analyzed [7]. The results showed that the K-Nearest Neighbor (KNN) algorithm outperforms other algorithms in most recognition tasks. Support Vector Machine (SVM) is another outstanding algorithm. A Multi-Class Hardware-Friendly Support Vector Machine (MC-HF-SVM), which uses fixed-point arithmetic for HAR instead of the typical floating-point arithmetic, has been proposed for sensor data [8]. Unlike the manual filtering features in previous algorithms, a systematic feature learning method that combines feature extraction with CNN training has also been proposed [9]. Subsequently, DeepConvLSTM networks outperformed previous algorithms in the Opportunity Challenge by an average of 4% of the F1 score [10]; the effects of parameters on the final result were...

show abstract

Particle Swarm Optimization (Pso) for Training Optimization on Convolutional Neural Network (Cnn)

Cited by 43 publications

References 13 publications

Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

Classification of Leaves Based on the Shape of Leaves Using Convolutional Neural Network Methods

Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors

Contact Info

Product

Resources

About