scholarly journals Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network

2021 ◽  
Vol 6 (1) ◽  
pp. 18
Author(s):  
Diny Melsye Nurul Fajri
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Environmentally Friendly ◽  
Machine Learning Method ◽  
Learning Method ◽  
Industrial Products ◽  
Kenaf Fiber ◽  
Plant Pests

Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.

Determination of thermodynamic state variables of liquids from its microscopic structure using an artificial neural network

Soft Matter ◽  
2020 ◽  
Author(s):  
Ulices Que-Salinas ◽  
Pedro Ezequiel Ramirez-Gonzalez ◽  
Alexis Torres-Carbajal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Distribution Function ◽  
Microscopic Structure ◽  
Machine Learning Method ◽  
Learning Method ◽  
State Variables ◽  
Thermodynamic State

In this work we implement a machine learning method to predict the thermodynamic state of a liquid using only its microscopic structure provided by the radial distribution function (RDF). The...

scholarly journals Lightweight Convolutional Neural Network Based Intrusion Detection System

2020 ◽  
pp. 808-817
Author(s):  
Vinh Pham ◽  
◽  
Eunil Seo ◽  
Tai-Myoung Chung
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Network Traffic ◽  
Detection System ◽  
Image Data ◽  
Training Data ◽  
Deep Packet Inspection ◽  
Detection Model

Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.

scholarly journals 24-hour cloud cover calculation using ground-based imager with machine learning

2021 ◽  
Author(s):  
Bu-Yo Kim ◽  
Joo Wan Cha ◽  
Ki-Ho Chang
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cloud Cover ◽  
Image Data ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Learning Methods ◽  
Human Eye ◽  
Machine Learning Methods

Abstract. In this study, image data features and machine learning methods were used to calculate 24-h continuous cloud cover from image data obtained by a camera-based imager on the ground. The image data features were the time (Julian day and hour), solar zenith angle, and statistical characteristics of the red-blue ratio, blue–red difference, and luminance. These features were determined from the red, green, and blue brightness of images subjected to a pre-processing process involving masking removal and distortion correction. The collected image data were divided into training, validation, and test sets and were used to optimize and evaluate the accuracy of each machine learning method. The cloud cover calculated by each machine learning method was verified with human-eye observation data from a manned observatory. Supervised machine learning models suitable for nowcasting, namely, support vector regression, random forest, gradient boosting machine, k-nearest neighbor, artificial neural network, and multiple linear regression methods, were employed and their results were compared. The best learning results were obtained by the support vector regression model, which had an accuracy, recall, and precision of 0.94, 0.70, and 0.76, respectively. Further, bias, root mean square error, and correlation coefficient values of 0.04 tenth, 1.45 tenths, and 0.93, respectively, were obtained for the cloud cover calculated using the test set. When the difference between the calculated and observed cloud cover was allowed to range between 0, 1, and 2 tenths, high agreement of approximately 42 %, 79 %, and 91 %, respectively, were obtained. The proposed system involving a ground-based imager and machine learning methods is expected to be suitable for application as an automated system to replace human-eye observations.

scholarly journals Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

2019 ◽  
Vol 490 (4) ◽  
pp. 4770-4777 ◽  
Author(s):  
M Kovačević ◽  
G Chiaro ◽  
S Cutini ◽  
G Tosti
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gamma Ray ◽  
Classification Performance ◽  
Previous Method ◽  
Machine Learning Method ◽  
Learning Method ◽  
Large Area ◽  
Γ Ray ◽  
Automatic Technique

ABSTRACT Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to develop an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope γ-ray instrument. The final result of this study increased the classification performance by about 80 ${{\ \rm per\ cent}}$ with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

scholarly journals ANALISIS TINGKAT KEBERHASILAN CRYOTERAPY MENGGUNAKAN NEURAL NETWORK

2019 ◽  
Vol 15 (2) ◽  
pp. 141-148
Author(s):  
Sri Rahayu ◽  
Fitra Septia Nugraha ◽  
Muhammad Ja’far Shidiq
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Side Effects ◽  
20Th Century ◽  
High Accuracy ◽  
Machine Learning Method ◽  
Learning Method ◽  
Good Classification ◽  
Auc Value

Human health is very important to always pay attention especially after someone has been declared suffering from an illness that can inhibit positive activities. One of the most feared diseases of the 20th century is cancer. This disease requires treatment that is quite expensive. Alternative treatments are cryotherapy or ice therapy. But cryotherapy also has side effects, it is necessary to do research on its success by taking into account certain conditions of the parameters. So the purpose of this study is to analyze the success of cryotherapy so that the dataset can be used as one of the benchmarks for the success of the cryotherapy tratment method. The method used in this study is the machine learning method of Neural Network with 500 training cycles, learning rate of 0,003 and momentum 0,9 which results in a good classification of obtaining quite high accuracy of 87,78% and AUC value of 0,955.

scholarly journals Batik Classification using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11416-11421
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Network ◽  
Image Data ◽  
High Accuracy ◽  
Learning Method ◽  
Philosophy Of Life ◽  
Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

scholarly journals Model Selection For Forecasting Rainfall Dataset

2021 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Amri Muhaimin ◽  
Hendri Prabowo ◽  
Suhartono
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Statistical Methods ◽  
Seasonal Pattern ◽  
Information Criteria ◽  
Rainfall Time Series ◽  
Machine Learning Method ◽  
Learning Method ◽  
Hourly Rainfall

The objective of this research is to obtain the best method for forecasting rainfall in the Wonorejo reservoir in Surabaya. Time series and causal approaches using statistical methods and machine learning will be compared to forecast rainfall. Time series regression (TSR), autoregressive integrated moving average (ARIMA), linear regression (LR), and transfer function (TF) are used as a statistical method. Feedforward neural network (FFNN) and deep feed-forward neural network (DFFNN) is used as a machine learning method. Statistical methods are used to capture linear patterns, whereas the machine learning method is used to capture nonlinear patterns. Data about hourly rainfall in the Wonorejo reservoir is used as a case study. The data has a seasonal pattern, i.e. monthly seasonality. Based on the cross-validation and information criteria, the results showed that DFFNN using the time series approach has a more accurate forecast than other methods. In general, machine learning methods have better accuracy than statistical methods. Furthermore, additional information is obtained, through this research the parameter that best to make a neural network model is known. Moreover, these results are also not in line with the results of M3 and M4 competition, i.e. more complex methods do not necessarily produce better forecasts than simpler methods.

Digraph states and their neural network representations

2021 ◽  
Author(s):  
Ying Yang ◽  
Huaixin Cao
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Body Problem ◽  
Theoretical Foundation ◽  
Rapid Development ◽  
Quantum States ◽  
Machine Learning Method ◽  
Learning Method ◽  
Many Body ◽  
Graph State

Abstract With the rapid development of machine learning, artificial neural networks provide a powerful tool to represent or approximate many-body quantum states. It was proved that every graph state can be generated by a neural network. In this paper, we aim to introduce digraph states and explore their neural network representations (NNRs). Based on some discussions about digraph states and neural network quantum states (NNQSs), we construct explicitly the NNR for any digraph state, implying every digraph state is an NNQS. The obtained results will provide a theoretical foundation for solving the quantum many-body problem with machine learning method whenever the wave-function is known as an unknown digraph state or it can be approximated by digraph states.

Sign in / Sign up

Export Citation Format

Share Document