scholarly journals Implementation of Deep Learning for Slump Optimization Based on Concrete Quality Using Convolutional Neural Network in PT. SELO PROGO SAKTI

2020 ◽  
Vol 5 (2) ◽  
pp. 83-88
Author(s):  
Hedi Pandowo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Experimental Design ◽  
Convolutional Neural Network ◽  
Processing Unit ◽  
Training Process ◽  
Average Accuracy ◽  
Graphical Processing ◽  
Intelligence Science

Deep Learning is part of the scientific field of Machine Learning and Machine Learning is part of Artificial Intelligence science. Deep Learning has extraordinary capabilities by using a hardware Graphical Processing Unit (GPU) so that the artificial requirement network can run faster than using a Personal Computer Unit (CPU). Especially in terms of object classification in images using existing methods in the Convolutional Neural Network (CNN). The method used in this research is Preprocessing and Processing of Input Data, Training Process in which CNN is trained to obtain high accuracy from the classification carried out and the Testing Process which is a classification process using weights and bias from the results of the training process. This type of research is a pre experimental design (pre experimental design). The results of the object image classification test with different levels of confusion in the Concrete database with the Mix Design K-125, K-150, K-250 and K-300 produce an average accuracy value. This is also relevant to measuring the failure rate of concrete or slump

scholarly journals Pengenalan Spesies Ikan Berdasarkan Kontur Otolith Menggunakan Convolutional Neural Network

2019 ◽  
Vol 2 (1) ◽  
pp. 41
Author(s):  
Heri Darmanto
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Gradient Descent ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Graphical Processing

Hasil sensus kehidupan laut pada tahun 2013 di seluruh dunia terdapat lebih dari 23.000 spesies dan masih banyak sekali spesies ikan yang belum diidentifikasi. Otolith merupakan organ yang sangat penting di belakang telinga ikan, karena melalui otolith ini dapat diketahui jenis ikan, pertumbuhan dan lingkungan, serta sejarah kehidupannya,  misalnya, umur, reproduksi, dan migrasi. Dengan semakin  canggihnya komputer dan pengolahan di bidang citra,  diharapkan  kemampuan  mengidentifikasi jenis  ikan  yang dimiliki oleh manusia bisa diadopsi  dan diterapkan pada perangkat komputer. Deep Learning saat ini semakin berkembang memanfaatkan sumber daya perangkat keras yang semakin canggih termasuk penggunaan GPU (Graphical Processing Unit) untuk perhitungan proses komputasi dengan akurasi yang lebih baik dan proses yang lebih cepat. Pada penelitian ini metode yang diusulkan, untuk keperluan klasifikasi ikan menggunakan metode Convolutional Neural Network dengan teknik Transfer Learning dari model Alexnet dan optimasi Momentum Stochastic Gradient Descent. Hasil eksperimen diperoleh akurasi sebesar 95.4% lebih tinggi dibanding metode Discriminant Analysis yang memiliki akurasi sebesar 92%.

scholarly journals Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 210 ◽  
Author(s):  
Zied Tayeb ◽  
Juri Fedjaev ◽  
Nejla Ghaboosi ◽  
Christoph Richter ◽  
Lukas Everding ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Classification Performance ◽  
Feature Engineering ◽  
Learning Models ◽  
Eeg Signals ◽  
Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

Convolutional Neural Network

2022 ◽  
pp. 1559-1575
Author(s):  
Mário Pereira Véstias
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Model ◽  
Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

CloudA: A Ground-Based Cloud Classification Method with a Convolutional Neural Network

2020 ◽  
Vol 37 (9) ◽  
pp. 1661-1668
Author(s):  
Min Wang ◽  
Shudao Zhou ◽  
Zhong Yang ◽  
Zhanhua Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Learning Ability ◽  
Visualization Method ◽  
Classification Methods ◽  
Recognition Method ◽  
Cloud Classification ◽  
Average Accuracy

AbstractConventional classification methods are based on artificial experience to extract features, and each link is independent, which is a kind of “shallow learning.” As a result, the scope of the cloud category applied by this method is limited. In this paper, we propose a new convolutional neural network (CNN) with deep learning ability, called CloudA, for the ground-based cloud image recognition method. We use the Singapore Whole-Sky Imaging Categories (SWIMCAT) sample library and total-sky sample library to train and test CloudA. In particular, we visualize the cloud features captured by CloudA using the TensorBoard visualization method, and these features can help us to understand the process of ground-based cloud classification. We compare this method with other commonly used methods to explore the feasibility of using CloudA to classify ground-based cloud images, and the evaluation of a large number of experiments show that the average accuracy of this method is nearly 98.63% for ground-based cloud classification.

scholarly journals Implementation of Machine Learning Using the Convolution Neural Network Method for Aglaonema Interest Classification

2021 ◽  
Vol 5 (1) ◽  
pp. 21-30
Author(s):  
Rachmat Rasyid ◽  
Abdul Ibrahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ornamental Plants ◽  
Convolution Neural Network ◽  
Neural Network Method ◽  
Study Results ◽  
Network Method ◽  
The Many

One of the wealth of the Indonesian nation is the many types of ornamental plants. Ornamental plants, for example, the Aglaonema flower, which is much favored by hobbyists of ornamental plants, from homemakers, is a problem to distinguish between types of aglaonema ornamental plants with other ornamental plants. So the authors try to research with the latest technology using a deep learning convolutional neural network method. It is for calcifying aglaonema interest. This research is based on having fascinating leaves and colors. With the study results using the CNN method, the products of aglaonema flowers of Adelia, Legacy, Widuri, RedKochin, Tiara with moderate accuracy value are 56%. In contrast, the aglaonema type Sumatra, RedRuby, has the most accuracy a high of 61%.

scholarly journals Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

2020 ◽  
Author(s):  
Monalisha Ghosh ◽  
Goutam Sanyal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Research Work ◽  
Research Field ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Ensemble Technique

Abstract ­­­­­­­­­­­­­­­­­­­­­­­­­­­ Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

scholarly journals Utilização de técnicas de Machine Learning e de Deep Learning para a predição de casos de internações causadas por dengue em municípios da Paraíba

2021 ◽  
Author(s):  
Ewerthon Dyego de Araújo Batista ◽  
Wellington Candeia de Araújo ◽  
Romeryto Vieira Lira ◽  
Laryssa Izabel de Araújo Batista
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Support Vector Regression ◽  
Multilayer Perceptron ◽  
Support Vector

Dengue é um problema de saúde pública no Brasil, os casos da doença voltaram a crescer na Paraíba. O boletim epidemiológico da Paraíba, divulgado em agosto de 2021, informa um aumento de 53% de casos em relação ao ano anterior. Técnicas de Machine Learning (ML) e de Deep Learning estão sendo utilizadas como ferramentas para a predição da doença e suporte ao seu combate. Por meio das técnicas Random Forest (RF), Support Vector Regression (SVR), Multilayer Perceptron (MLP), Long ShortTerm Memory (LSTM) e Convolutional Neural Network (CNN), este artigo apresenta um sistema capaz de realizar previsões de internações causadas por dengue para as cidades Bayeux, Cabedelo, João Pessoa e Santa Rita. O sistema conseguiu realizar previsões para Bayeux com taxa de erro 0,5290, já em Cabedelo o erro foi 0,92742, João Pessoa 9,55288 e Santa Rita 0,74551.

scholarly journals Improving Water Quality Assessment through Anomaly Detection Using Hybrid Convolutional Neural Network Approach

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Water Quality ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Machine Learning Algorithms ◽  
Quality Analysis

<p>Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.</p>

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