scholarly journals Image Processing Failure and Deep Learning Success in Lawn Measurement

2020 ◽  
Author(s):  
J. Wilkins Wilkins ◽  
M. V. Nguyen Nguyen ◽  
B. Rahmani Rahmani
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Area Measurement ◽  
Learning Methods ◽  
Hierarchical Networks ◽  
Processing Methods ◽  
Learning Success ◽  
Segmented Images

Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, image processing and deep learning methods were used to find the best way to measure the lawn area. Three image processing methods using OpenCV compared to convolutional neural network, which is one of the most famous, and effective deep learning methods. We used Keras and TensorFlow to estimate the lawn area. Convolutional neural network or shortly CNN shows very high accuracy (94-97%). In image processing methods, thresholding with 80-87% accuracy and edge detection are the most effective methods to measure the lawn area while the method ofcontouring with 26-31% accuracy does not calculate the lawn area successfully. We may conclude that deep learning methods, especially CNN, could be the best detective method comparing to image processing learning techniques.

scholarly journals Application of Convolutional Neural Network In LAWN Measurement

2021 ◽  
Vol 12 (1) ◽  
pp. 1-8
Author(s):  
J. Wilkins ◽  
M. V. Nguyen ◽  
B. Rahmani
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Good Method ◽  
High Accuracy ◽  
Area Measurement ◽  
Learning Method ◽  
Hierarchical Networks ◽  
Segmented Images ◽  
State Of Art

Lawn area measurement is an application of image processing and deep learning. Researchers used hierarchical networks, segmented images, and other methods to measure the lawn area. Methods’ effectiveness and accuracy varies. In this project, deep learning method, specifically Convolutional neural network, was applied to measure the lawn area. We used Keras and TensorFlow in Python to develop a model that was trained on the dataset of houses then tuned the parameters with GridSearchCV in ScikitLearn (a machine learning library in Python) to estimate the lawn area. Convolutional neural network or shortly CNN shows high accuracy (94 -97%). We may conclude that deep learning method, especially CNN, could be a good method with a high state-of-art accuracy.

scholarly journals A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 81
Author(s):  
Jianbin Xiong ◽  
Dezheng Yu ◽  
Shuangyin Liu ◽  
Lei Shu ◽  
Xiaochan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Plant Species ◽  
Image Recognition ◽  
Recurrent Neural Network ◽  
Plant Diseases ◽  
Learning Methods ◽  
Smart Agriculture ◽  
Important Branch

Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed.

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- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

scholarly journals Deep Learning Convolutional Neural Network for Defect Identification and Classification in Woven Fabric

2021 ◽  
Vol 1 (2) ◽  
pp. 9-13
Author(s):  
Subrata Das ◽  
Sundaramurthy S ◽  
Aiswarya M ◽  
Suresh Jayaram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Textile Industry ◽  
Woven Fabric ◽  
Defect Identification ◽  
Complete Inspection ◽  
Hidden Layer ◽  
Deep Learning Neural Network

Inspection is the most important role in textile industry which declares the quality of the apparel product. Many Industries were improving their production or quality using Artificial Intelligence. Inspection of fabric in textile industry takes more time and labours. In order to reduce the number of labours and time taken to complete inspection, computerized image processing is done to identify the defects. It gives the accurate result in less time, thereby saves time and increases the production. The convolutional neural network in deep learning is mainly used for image processing for defect detection and classification. The high quality images are given as input, and then the images were used to train the deep learning neural network. Thewoven fabricdefects such as Holes, Selvedge tails, Stains, Wrong drawing and Snarlswere identified by using Convolutional Neural Network. The sample images were collected from the SkyCotex India Pvt.Ltd. The sample images were processed in CNN based machine learning ingoogle platform; the network has a input layer, n number of hidden layer and output layer. The neural network is trained and tested with the samples and the result obtained is used to calculate the efficiency of defect identification.

scholarly journals A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 116 ◽  
Author(s):  
Yonghua Zhu ◽  
Xun Gao ◽  
Weilin Zhang ◽  
Shenkai Liu ◽  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Text Classification ◽  
Business Value ◽  
The Internet ◽  
Information Analysis ◽  
Target Information ◽  
Learning Methods ◽  
Daily Lives

The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods.

IMPLEMENTASI DEEP LEARNING MENGGUNAKAN FRAMEWORK TENSORFLOW DENGAN METODE FASTER REGIONAL CONVOLUTIONAL NEURAL NETWORK UNTUK PENDETEKSIAN JERAWAT

2018 ◽  
Vol 23 (2) ◽  
pp. 89-102
Author(s):  
Yunita Aulia Hasma ◽  
Widya Silfianti
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network

Jerawat sering dialami oleh kaum wanita maupun pria dari usia remaja hingga dewasa. Banyak rumah sakit dan klinik kecantikan yang dapat di datangi oleh para penderita untuk memeriksakan jerawat tersebut. Penelitian ini merupakan implementasi dari pendeteksian jerawat menggunakan image processing dan secara realtime, lalu sistem akan mengklasifikasikan jerawat yang ada pada wajah. Jerawat yang dapat dikenali oleh sistem ini yaitu jerawat, bekas, dan pus. Sistem deteksi dan klasifikasi ini dibuat dengan metode deep learning dengan menggunakan bahasa pemrograman Python, yang dibantu dengan menggunakan framework TensorFlow dengan model Faster R-CNN. Sistem ini hanya dapat berjalan di laptop dengan memiliki Python versi 3.6 di dalamnya dan telah memliki library Numpy, TkInter, Matplotlib, dan OpenCV dan juga memiliki kamera pada laptop yang digunakan agar dapat menjalankan sistem secara realtime yang didukung dengan GPU yang memadai. Perancangan alur aplikasi menggunakan flowchart diagram. Hasil uji terhadap sistem menggunakan perbandingan objek yang terdeteksi dengan yang seharusnya lalu dibagi dan dikalikan dengan seratus persen. Hasil yang didapat dari pengujian cukup baik menggunakan metode deep learning.

Identifying Emotion on Indonesian Tweets using Convolutional Neural Networks

2021 ◽  
Vol 5 (3) ◽  
pp. 584-593
Author(s):  
Naufal Hilmiaji ◽  
Kemas Muslim Lhaksmana ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
Classification Model ◽  
Learning Methods ◽  
Expected Performance

especially with the advancement of deep learning methods for text classification. Despite some effort to identify emotion on Indonesian tweets, its performance evaluation results have not achieved acceptable numbers. To solve this problem, this paper implements a classification model using a convolutional neural network (CNN), which has demonstrated expected performance in text classification. To easily compare with the previous research, this classification is performed on the same dataset, which consists of 4,403 tweets in Indonesian that were labeled using five different emotion classes: anger, fear, joy, love, and sadness. The performance evaluation results achieve the precision, recall, and F1-score at respectively 90.1%, 90.3%, and 90.2%, while the highest accuracy achieves 89.8%. These results outperform previous research that classifies the same classification on the same dataset.

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